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Over the past few years, I’ve interviewed some of the smartest people in data. You’ll hear three real stories of people with no experience landing their first data job, and exactly how they did it.
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⌚ TIMESTAMPS
00:00 – Three real stories of breaking into data with no experience
00:38 – Thomas: From high school teacher to data role in 61 days
13:52 – Kadesha Bryant: From warehouse work to senior data roles through networking
24:35 – Rachel Finch: Escaping the night shift and landing a BI role in 95 days
39:45 – The shared pattern behind all three data career pivots
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Transcripts
Over the past 1000 days, I've had the opportunity to
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interview some of the smartest and
brightest minds in the data world.
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They've given really great advice
on how to actually land a data job.
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In today's episode, you'll hear three
real stories of people with no experience.
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Landing their first data job.
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They'll teach you what skills to
learn, how to approach the job hunt,
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and the secret to getting hired
sometimes in less than 60 days.
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So without further ado, let's
go ahead and get into it.
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The first story is Thomas.
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And Thomas was a frustrated high school
math teacher who did not really like
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his job and wanted to pivot into data,
and 61 days later, he was able to do so.
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Here's exactly how he did it.
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You ultimately joined the accelerator.
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And you landed a job as this senior
reimbursement analyst pre pretty quickly.
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Do you know how fast you ended that job?
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Speaker 5: Uh, so I started the
program in April, we said end of April.
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Yep.
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And I got that job, I think I had
the first interview in the middle of
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June, so about a month and a half.
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I guess I would say
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Speaker 4: I, I had from your start day of
the accelerator to when you told us that
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you landed the job, I have it as 61 days.
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Yeah.
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So less, less than two months.
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And you had been doing, like for
instance, like you said, this data
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science bootcamp through Rutgers, like
all, yeah, not all of last year, but
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you had done it the year previous.
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Mm-hmm.
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So basically li like I said,
you are so close landing a job,
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you just need the s SPN method.
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What is, do you feel like that's what
made the difference for you to like to,
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to have land that job within two months?
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Speaker 5: Absolutely.
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I, I would, I would say definitely
'cause I felt like I had the
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skills, like we just talked about.
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I just wasn't networking correctly.
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Uh, I wasn't doing what I had to do
on LinkedIn and you don't realize.
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Coming from the education world,
LinkedIn doesn't really exist.
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You, you apply for jobs and you go on the
interviews and you bring, you know, stuff
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that you had done in other classrooms
or, or in my case, 'cause it was right
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outta college or in student teaching.
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And, and that's pretty much it.
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Whereas for this, this
was all brand new to me.
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And the bootcamp that I took while all
good and well, I learned these skills.
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I had no idea what to do after.
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There was no, you know, you should do this
to network with X, Y, and Z, or this is
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how you should show off your projects.
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It was just, we did a lot of projects
and a lot of, uh, little tasks or
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homework assignments they called.
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Uh, but that was all on GitHub.
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And like you and I had talked about in
that first call, you're like, that's
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not really gonna do anything for you.
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'cause no, um, employer or hiring
manager is gonna sift through a bunch
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of code on your GitHub portfolio.
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Like it's just not gonna do anything.
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So I think SPN definitely
made the difference for me.
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Where learned the skills, made
these projects, and then was able
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to network and, and show off these
projects in a really cool way.
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Speaker 4: I, I think so too.
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I think, I think you were so close.
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You had all the skills, you just
needed the portfolio, uh, and Right.
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And the networking.
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When I went through your
LinkedIn today to like kind of
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go through your whole journey.
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Uh, you had posted once about the,
the data science bootcamp from Rutgers
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and it was at, at the very end.
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Um, I think it was maybe just
like a certificate or something.
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And that's so opposed to how we
do it inside of data analytics
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accelerator where like literally
day one I'm like post on LinkedIn.
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Post On LinkedIn, you finish your
first project post on LinkedIn.
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Uh, so I think that was
one of the big things.
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And that's ultimately
how you found this job.
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Correct.
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Was someone reposted it on LinkedIn?
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Speaker 5: Yeah.
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So.
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Like you said, I had really never
posted on LinkedIn throughout
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that bootcamp, which is obviously
wasn't doing me any good.
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Um, and then started posting on
LinkedIn through the bootcamp
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or through, um, our program.
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And then I just kind of followed people
who you interacted with on LinkedIn and
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found a lot of them to be posting jobs.
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And the one guy, I'm sorry, I can't
give him credit, I don't really
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remember his name or who it was exactly.
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He posted, I think like 10 or so remote
jobs, either weekly, every few days.
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And I would just apply to them if I
thought I was a decent candidate for
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the job, even if I wasn't really like
a super great fit in layman's terms.
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I, I just, I thought might as well apply.
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Can't hurt to apply.
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Um, so I applied, uh, to this
specific job and, and I was
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able to, to get an interview.
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I was honestly kind of shocked that I
got the interview with them, but that's,
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that's what I'm saying, like you just.
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You never know.
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And I think it's really important to
apply and, and look at these posts.
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Uh, there's a lot of, you know, anecdotal
stuff on LinkedIn and, and you have talked
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about posting some stuff like that too,
um, in the, in the data career jumpstart.
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But there are also a lot of
people who are trying to help us,
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like people that are looking for
jobs where they're posting jobs.
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And I think that's really important to
look for and, and not to get too bogged
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down in, oh, this isn't for me because.
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Really, it's for everybody.
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Everybody's doing it.
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You know?
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Speaker 4: I think you had also
mentioned that that job that you
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ended up landing required, what, two
to three years of, of experience?
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Speaker 5: Yeah.
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It was two to three years of some
healthcare or medical experience,
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which obviously I'm a math teacher.
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I did, I just did not have.
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Um, and I, I can't, I think that's what
it said, but it, it said that in the
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actual job description, but in the.
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Original post on LinkedIn
by the hiring manager.
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It said zero to two
years experience needed.
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So I was like, oh, well the original
post says zero to two years.
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I don't really care what the job
description says right now, lemme
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just apply and see what happens.
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So I, I think, don't get discouraged
by a lot of what job descriptions say.
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You know, a lot of that
could come from the top down.
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It might not even come
from the hiring manager.
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It could just come from what
the, the company as a whole want
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that job description to say.
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Speaker 4: At the end of the day,
job descriptions are really more
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wishlist than they are requirements.
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So if you fit like 65 to 70%,
uh, maybe even 50% sometimes, you
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know, go ahead and apply, right?
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Because you never know what might happen.
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And that was true.
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For, for you in, in this case,
do you remember if you, was it
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like a LinkedIn, uh, easy apply?
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Was it that you, did you
apply on their website?
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Speaker 5: Um, I applied on
their website, so it was a link.
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I just clicked on the link and
I applied on the Rev website.
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It was really simple, and I think
you and I had actually talked about
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this in the original call that we had
in March, or even, I think I talked
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to you again in April or so, right
around when I joined the program.
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It, it was a
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Speaker 4: DM you sent me, I think.
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Speaker 5: Yeah.
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And it was, and you said just always apply
on, uh, the actual website if you can.
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They're just more likely to
look at that than the LinkedIn,
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um, like easy apply algorithm.
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Speaker 4: It's, it's super true.
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Um, having posted a job on LinkedIn
jobs, let me tell you, uh, LinkedIn,
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you need to hire a data scientist to
make your algorithm for candidates a lot
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better because I got over 550 applicants
and the top applicants were not on the
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first two pages, I'll tell you that.
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Like who they thought was relevant.
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I was like, this person's not
relevant, so that's great.
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Did you do anything special cover letter,
send a cold message, anything like that?
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Speaker 5: Definitely sent a cold message.
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And it was funny because, um, the person
who interviewed me first, uh, I sent
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a cold message to her boss and then
she said, you know, honestly, your,
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your resume was just passed to me.
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Like I, someone got a message from
me and that's how I got your resume.
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And I decided to, you know, interview.
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And I was like, well, that's awesome.
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I guess that worked out for me.
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Um, but I don't think
I did a cover letter.
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Uh, we might have even
talked about this, I think.
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The cover letter is,
well, they're important.
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I guess they're way more likely
to just read your cold message if
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that's what you're sending them
than they are your cover letter.
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Speaker 4: Cold messages are
the new, uh, cover letter.
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I think cover letters are kind of dead
and if you can send Yeah, a cold message
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where it's like, I don't have to read
one page of stuff that's just mostly
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fluff that you use chat GPT to write,
and instead you can tell me in like.
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Three to four lines, who you
are, why I should care about you.
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I think that's some directly to my inbox.
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I think that's way more impactful.
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That's awesome.
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I didn't realize you sent a cold message.
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I think.
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Yeah, that's, I'm trying to figure
out like, you know, when, when Thomas
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is applying, I know you're a great
candidate, you know, you're a great
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candidate, but how do you convince this
recruiter and this hiring manager when
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they have, you know, 500 other candidates
that you're the right candidate?
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And I think the cold message is one,
and then probably your portfolio
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helped stand out a little bit.
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Speaker 5: Yeah, I would think so.
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Uh, I think just going back to the
cold messages, like I was sending
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one to every job that I applied to,
uh, or at least trying to, trying
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to find someone that I could.
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And I, I believe there's a page or
a couple pages on our, on our, uh,
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like in our book of materials that
you give us, where it just kind
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gives you like an outline of what
you should say to these people.
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And that's what I was,
I had it bookmarked.
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I was going back to it every single time.
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Um, but yeah, they did talk about my, um.
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Uh, portfolio.
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Um, I think it was probably
a sticking out point.
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Uh, you know, the person that interviewed
me first said it was definitely super
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interesting and like I said to you, uh,
she thought that just based on that, that
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my analytical skills absolutely qualified
for the job that they were looking for.
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Speaker 4: That's actually
really cool because, um.
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You didn't have any healthcare experience
prior to this, but one of the things I
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tried to do when I designed the bootcamp
was each module has like a different
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industry theme, and so in module five we,
we cover some healthcare data using sql.
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So you know, you, you'd maybe never
actually, like in a workplace, looked
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at healthcare records, but in this
bootcamp with, we had looked over,
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I think there was like 2 million
rows in that, in that SQL data.
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Set that we, we analyze.
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So you had, you had at least some,
you created your own healthcare
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experience at the end of the day,
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Speaker 5: right?
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I think I actually said that.
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I was like, yeah.
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In my portfolio.
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Uh, I, you know, I had this
healthcare project that we worked on.
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Uh, you know, I tried to
pull from family members too.
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I was like, I have some family that
works in healthcare and, you know.
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You don't wanna necessarily lie because
they could ask you follow up questions,
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but you certainly wanna make your
knowledge look a little bit better.
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And I, I think that's what I tried to
do, especially using that project that
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00:09:56,145 --> 00:09:58,095
we had worked on in the, in the class.
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Speaker 4: Hey, experience is experience.
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No one can take it away.
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You can just describe it as it is,
and they can decide whether they, they
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00:10:04,815 --> 00:10:08,535
think it qualifies enough, but, uh,
it's always good to get that out there.
220
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Um, even with that, I,
I think this is true.
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I haven't, I haven't talked about the.
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I haven't talked to you about this before.
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Um, but, uh, I think after this
first interview, this, this
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timeline maps up a little bit.
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Um, you went into our community and
you said, just finished the capstone
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and I had my first interview, uh,
this week, however, it seems like
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I don't have enough healthcare
experience, so I'm not too confident.
228
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If anything else, it was a
good interview experience.
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I'm continuing to apply for
jobs and sending cold messages.
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Then you say this great line.
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Some days it's hard to not feel
defeated, but definitely trying
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to stay as positive as possible.
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Hoping to land something soon.
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That, was that the first
interview for this job?
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Speaker 5: Yeah, that was the
first interview for that job.
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And I'm laughing thinking
about, thinking back to that.
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'cause I'm really, I got off the call
and I was like, wow, I, I have no shot.
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I was like, I don't have
this healthcare experience.
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And uh, it just kind of all worked out.
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I think that's the important thing.
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Like we talked about this a
little bit, just go on these
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interviews and kind of be yourself.
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Um, I really talked about my
willingness to learn and want to learn.
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And, um, I guess they liked that.
245
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Um, and I, again, I was really
surprised and I was after that,
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00:11:21,210 --> 00:11:23,820
got the second interview and I
was pretty nervous for that too.
247
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I was like, I wonder, I don't
even know why they're interviewing
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me a second time right now.
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Um, but that interview, and I said it,
when I got off it, I was like, I really
250
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think that I might have just gotten this.
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And it wasn't anything technical.
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Uh, they did ask a little
bit about my experience, but.
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You just kind of go into these interviews
and you kind of feel the vibe with the
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people that you're going to work for.
255
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And I just thought the vibe was great.
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You know, I, I thought they'd be
great people to work for, and it
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got me really excited about it.
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And you just say, here I am.
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Speaker 4: I, I think
that's so interesting.
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And, and I love that, that the interviews,
sometimes they're super scary, but a
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lot of the times they're just like,
okay, does this person seem like they.
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Have enough technical skills and
are they able to learn the rest?
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I know that's one of the
things you mentioned.
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It's like maybe, I don't know healthcare
yet, but I'm, I'm willing to learn that.
265
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Um, I wanna go back to that phrase.
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Sometimes it, it is hard
to not feel defeated.
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Uh, what were you feeling
when, when you posted that?
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00:12:19,260 --> 00:12:20,490
Speaker 5: I think I
was a little bit upset.
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Um, probably defeated honestly,
because I, I felt like this.
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I'm sure there are a lot of people like
me out there where, you know, you're
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00:12:29,969 --> 00:12:33,060
applying to so many jobs and you're
not hearing back that when you get that
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00:12:33,060 --> 00:12:37,140
first interview with that company, no
matter what company it is, you feel
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like, all right, this is my shot.
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I gotta get this.
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Um, and that's how I
felt with this company.
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And I, like I said to you, I, I feel
like the first interview didn't go.
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00:12:49,050 --> 00:12:51,900
As well as I, not that it didn't go
well, it just, I know what they were
278
00:12:51,900 --> 00:12:55,710
expecting and I didn't think that
was me, so that it kind of stunk.
279
00:12:55,980 --> 00:13:00,660
But at the same time, like I knew how
badly I wanted to change what I was doing
280
00:13:00,660 --> 00:13:04,890
or change my career path, that it was
still driving me because, you know, I,
281
00:13:04,890 --> 00:13:07,439
I had talked to my family about it and
they're like, well, even if you don't
282
00:13:07,439 --> 00:13:08,640
get it, you're not just gonna stop.
283
00:13:08,790 --> 00:13:09,660
And I was like, yeah, you're right.
284
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There's really no point
in feeling defeated.
285
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'cause I'm not gonna just stop.
286
00:13:12,090 --> 00:13:14,580
I, you know, you wanna
keep going until you.
287
00:13:15,075 --> 00:13:18,405
Get that ultimate goal of getting a
new job, and I think that's where the
288
00:13:18,405 --> 00:13:20,060
staying positive portion is, is really.
289
00:13:21,195 --> 00:13:21,735
Important.
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00:13:21,855 --> 00:13:24,465
Speaker 4: I hope you learned
a lot from Thomas's story about
291
00:13:24,465 --> 00:13:27,915
staying positive, sending cold
messages, and having a portfolio.
292
00:13:27,945 --> 00:13:31,035
Those things are super important
and he's living proof that it's
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00:13:31,035 --> 00:13:34,485
still possible to pivot into a
data job even in today's economy.
294
00:13:34,545 --> 00:13:38,235
By the way, if you want more stories
like these and just solid data, career
295
00:13:38,235 --> 00:13:39,615
advice overall, consider joining.
296
00:13:40,245 --> 00:13:40,875
Free newsletter.
297
00:13:40,935 --> 00:13:45,255
I send out tips every Wednesday to
27,000 other data analysts just like you,
298
00:13:45,255 --> 00:13:46,815
and I think you might really enjoy it.
299
00:13:46,875 --> 00:13:50,175
So go to data career
jumpstart.com/newsletter,
300
00:13:50,235 --> 00:13:52,425
or you can find the link in
the description down below.
301
00:13:52,485 --> 00:13:55,905
Our next real story is Kadesha
Bryant and Khas story starts with
302
00:13:55,905 --> 00:13:59,595
her working as a technician for
the military, and right after that
303
00:13:59,595 --> 00:14:01,275
she had become a warehouse worker.
304
00:14:01,335 --> 00:14:03,375
But after some networking magic.
305
00:14:03,455 --> 00:14:06,965
She was actually able to land
some pretty senior data jobs.
306
00:14:06,995 --> 00:14:10,565
So here's her strategy and exactly
how you can implement it for yourself.
307
00:14:10,685 --> 00:14:13,715
Speaker 6: So in the Navy, I started
off as a calibration technician,
308
00:14:14,075 --> 00:14:15,755
so very hands-on technical.
309
00:14:16,205 --> 00:14:21,935
Uh, then I moved up into production
supervisor, and then right before or after
310
00:14:21,935 --> 00:14:25,265
I left in 2020, I went back into school.
311
00:14:25,830 --> 00:14:30,720
And I was expecting to walk into
a hundred thousand dollars jobs,
312
00:14:31,110 --> 00:14:32,580
and that just did not happen.
313
00:14:32,700 --> 00:14:37,140
So I just went back into, I went
back into school and started
314
00:14:37,260 --> 00:14:39,270
delivering pizzas for Domino's.
315
00:14:39,630 --> 00:14:43,470
I was doing that for about a few months,
then started delivering packages for
316
00:14:43,470 --> 00:14:48,660
Amazon, and then did that for a few months
and started working at The Warehouse.
317
00:14:49,050 --> 00:14:49,380
Yeah.
318
00:14:50,055 --> 00:14:51,675
The Amazon warehouse or just what?
319
00:14:51,675 --> 00:14:52,335
What type of warehouse?
320
00:14:52,815 --> 00:14:56,865
Oh, it was a alcohol
distributor, so very heavy.
321
00:14:56,865 --> 00:15:01,905
Just imagine big wine cases,
alcohol cases, so doing
322
00:15:01,905 --> 00:15:03,285
that for 10, 12 hours a day.
323
00:15:04,095 --> 00:15:05,145
That sounds exhausting.
324
00:15:05,895 --> 00:15:06,675
It was exhausting.
325
00:15:06,675 --> 00:15:07,545
I was in great shape.
326
00:15:07,545 --> 00:15:08,595
I appreciated that.
327
00:15:08,595 --> 00:15:10,245
But otherwise was terrible.
328
00:15:10,695 --> 00:15:11,445
Speaker 4: Yeah.
329
00:15:11,475 --> 00:15:11,895
Okay.
330
00:15:12,075 --> 00:15:14,895
Now walk us through that
journey a little bit.
331
00:15:14,895 --> 00:15:19,365
How did you get from, you know,
delivering pizza dominoes to landing
332
00:15:19,365 --> 00:15:20,805
a senior data engineering position?
333
00:15:20,835 --> 00:15:24,495
Speaker 6: Okay, so I first learned
about the data engineering space.
334
00:15:24,495 --> 00:15:28,545
I would say maybe like Spring 21 when
I was discussing it with a professor.
335
00:15:28,575 --> 00:15:30,915
'cause I was like trying to
figure out what are some good
336
00:15:30,915 --> 00:15:32,295
job opportunities for me.
337
00:15:33,015 --> 00:15:35,835
And she mentioned data
analytics, so I researched it.
338
00:15:36,405 --> 00:15:40,395
Went on Google, I think my first month
was trying to decipher the difference
339
00:15:40,395 --> 00:15:42,315
between a data scientist and data analyst.
340
00:15:42,735 --> 00:15:47,475
Then I settled on analyst, I
self-study for summer of 21.
341
00:15:47,895 --> 00:15:53,445
Uh, pretty much sql, Tableau with some
power BI created a decent portfolio.
342
00:15:53,805 --> 00:15:56,895
And then in the midst of while I was
doing that, I was also working at
343
00:15:56,895 --> 00:15:58,935
the warehouse 10 to 12 hours a day.
344
00:15:59,595 --> 00:16:01,875
And the thing about that warehouse
gig, it was like a bait and
345
00:16:01,875 --> 00:16:04,815
switch, and I eventually just quit.
346
00:16:05,190 --> 00:16:10,260
With no backup plan, and I
started studying data for about
347
00:16:10,260 --> 00:16:12,750
six hours a day, six days a week.
348
00:16:13,080 --> 00:16:14,250
Just 'cause I loved it so much.
349
00:16:14,250 --> 00:16:18,030
It was just really cool to finally
find something where I can learn
350
00:16:18,030 --> 00:16:18,930
and get better at something.
351
00:16:20,234 --> 00:16:23,385
On my own without having to
like, you know, in school or
352
00:16:23,385 --> 00:16:25,425
pay tuition or things like that.
353
00:16:26,175 --> 00:16:29,655
So it was just a really cool
field to get into and I knew
354
00:16:29,655 --> 00:16:34,035
internships were starting to, were
gonna pop up late or early August.
355
00:16:34,035 --> 00:16:35,505
So I was preparing for that.
356
00:16:35,805 --> 00:16:41,984
I applied, I got my resume beefed
up pretty much, and I got my
357
00:16:41,984 --> 00:16:44,084
first gig at Cox Communications.
358
00:16:44,730 --> 00:16:49,830
I did that and while doing that,
also found DCJ and started doing
359
00:16:49,830 --> 00:16:53,550
a lot more upskilling in my free
time and started doing a lot of
360
00:16:53,550 --> 00:16:57,060
networking also in 2022, so last year.
361
00:16:57,540 --> 00:17:03,330
And so during that time, you actually hit
me up at some point and said if I saw some
362
00:17:03,330 --> 00:17:06,285
like job that Mark Freeman posted in the.
363
00:17:06,710 --> 00:17:07,910
DCJ Discord.
364
00:17:08,420 --> 00:17:11,359
And then you actually ended
up getting both of us to talk.
365
00:17:11,450 --> 00:17:16,609
And basically I submitted my
information and I applied to it.
366
00:17:16,760 --> 00:17:21,260
I got the job and I was able to start
in, I'd say the fall of last year.
367
00:17:21,800 --> 00:17:25,220
And so while I was doing that,
I was also networking like
368
00:17:25,460 --> 00:17:27,170
ridiculously the entire time.
369
00:17:27,170 --> 00:17:30,620
So I was having coffee
chats at least twice a week,
370
00:17:30,800 --> 00:17:32,240
sometimes way more than that.
371
00:17:32,600 --> 00:17:37,850
I, my thing was I wanted to learn from
people who were way more advanced than me.
372
00:17:37,880 --> 00:17:41,750
I wanna learn from people who
were steps ahead than me and know
373
00:17:41,750 --> 00:17:44,960
exactly how they got there and
what I need to do to get there.
374
00:17:45,470 --> 00:17:52,130
And so with that type of approach, I never
really approached them with the idea of,
375
00:17:52,130 --> 00:17:56,720
I'm hoping to get a job from them, but you
know, eventually they would think about me
376
00:17:56,720 --> 00:18:00,170
for, you know, roles or opportunities and.
377
00:18:00,930 --> 00:18:01,950
It was just insane to me.
378
00:18:01,950 --> 00:18:04,650
So it was really like crazy networking.
379
00:18:04,650 --> 00:18:05,880
Crazy upskilling.
380
00:18:06,390 --> 00:18:11,490
Sometime last year someone posted about
something about Booz Allen and my veterans
381
00:18:11,610 --> 00:18:16,380
data science group and veterans data
science and machine learning work group,
382
00:18:16,950 --> 00:18:19,230
and I applied for it and I got the gig.
383
00:18:19,260 --> 00:18:20,190
So I was it.
384
00:18:20,190 --> 00:18:22,020
It's crazy like my past two jobs.
385
00:18:22,379 --> 00:18:28,230
It spurned out of a discord, burned out
a discourse, burned out of networking.
386
00:18:28,230 --> 00:18:28,679
It's insane.
387
00:18:28,679 --> 00:18:30,000
So that's how I got this job.
388
00:18:30,060 --> 00:18:33,000
So that's like a kind of a,
in a nutshell how I got here.
389
00:18:33,510 --> 00:18:34,110
Speaker 4: I love it.
390
00:18:34,110 --> 00:18:34,530
Thank you.
391
00:18:34,530 --> 00:18:35,399
Thank you for that summary.
392
00:18:35,399 --> 00:18:36,179
That was really great.
393
00:18:36,240 --> 00:18:39,659
There's a lot of stuff that I want, I
wanna dive a little bit deeper into, and
394
00:18:39,659 --> 00:18:43,080
I'm gonna start, sometimes I build up
when I do these podcasts, like we build
395
00:18:43,080 --> 00:18:44,820
up to kind of the advice at the end.
396
00:18:44,820 --> 00:18:46,500
But I kind of wanna reverse that with you.
397
00:18:46,860 --> 00:18:49,320
So you did two coffee chats a week.
398
00:18:49,830 --> 00:18:51,459
Tell us like, do you
think that was worth it?
399
00:18:52,560 --> 00:18:53,130
Oh, absolutely.
400
00:18:53,130 --> 00:18:54,450
Yeah, absolutely worth it.
401
00:18:54,510 --> 00:18:57,690
Speaker 6: Honestly, I think a lot
of people, like they'll focus on,
402
00:18:57,780 --> 00:19:00,810
you know, followers or things like
that when posting on LinkedIn.
403
00:19:00,810 --> 00:19:03,870
But no, it's like the, it's the
networking or the folks that you're
404
00:19:03,870 --> 00:19:06,030
talking to behind the scenes.
405
00:19:06,030 --> 00:19:08,760
That's really where your
real network is built.
406
00:19:09,270 --> 00:19:11,490
So it was exhausting at times.
407
00:19:11,490 --> 00:19:14,460
There were times where I
kind of overbook myself, but.
408
00:19:14,895 --> 00:19:19,725
I've advanced way faster and much
more quicker than had I just tried
409
00:19:19,725 --> 00:19:21,675
to be to myself and not network 'em.
410
00:19:21,705 --> 00:19:23,865
Speaker 4: Yeah, I think
network is really key.
411
00:19:23,925 --> 00:19:25,785
I mean, that's one of the
things I really believe in.
412
00:19:26,265 --> 00:19:29,235
And one of the things why I think
Data Career Jumpstart is a little bit
413
00:19:29,235 --> 00:19:33,075
different, you know, and the SPN method
skills portfolio network, you have to
414
00:19:33,075 --> 00:19:37,695
have all three because the networking is
what ends up landing you both your jobs
415
00:19:37,695 --> 00:19:39,075
kind of at the end of the day, right?
416
00:19:39,075 --> 00:19:42,585
And it's like you can spend
all this time applying to jobs.
417
00:19:42,585 --> 00:19:43,185
You could spend.
418
00:19:43,889 --> 00:19:46,500
This time filling out all
these applications, perfecting
419
00:19:46,500 --> 00:19:47,850
your resume, all that stuff.
420
00:19:48,209 --> 00:19:50,610
But if you don't have that,
that could lead you nowhere.
421
00:19:50,850 --> 00:19:52,860
But a lot of the times, the
network is actually what leads
422
00:19:52,860 --> 00:19:54,149
you to the promised land.
423
00:19:54,480 --> 00:19:57,449
At the end of the day,
I'm reading a message.
424
00:19:57,824 --> 00:20:03,945
I sent you on March 29th, 2022, with
I guess your kind of former boss,
425
00:20:04,485 --> 00:20:07,334
and I basically introduced you to
Mark because you were doing awesome
426
00:20:07,334 --> 00:20:10,425
things for me and you're doing awesome
things for your fellow students
427
00:20:10,425 --> 00:20:11,925
in my program and my community.
428
00:20:11,925 --> 00:20:15,915
And so I introduced you to Mark, and then
you ended up landing that job at Humu.
429
00:20:15,915 --> 00:20:18,344
And anyways, it's just crazy to me like.
430
00:20:19,110 --> 00:20:19,560
You.
431
00:20:19,560 --> 00:20:21,630
You could have had the world's
best resume, but if you weren't
432
00:20:21,630 --> 00:20:23,880
networking, people don't really
know or don't really care.
433
00:20:23,880 --> 00:20:24,180
Right.
434
00:20:24,240 --> 00:20:25,290
Yeah, it's a good point.
435
00:20:25,290 --> 00:20:28,770
'cause even if you have the network,
you have to have some substance behind
436
00:20:28,770 --> 00:20:32,460
it because especially with that job
opening, that was my friend Mark.
437
00:20:32,460 --> 00:20:32,790
Right?
438
00:20:33,030 --> 00:20:34,500
And I wanna have a good
relationship with Mark.
439
00:20:34,500 --> 00:20:36,360
I don't want him, I don't
wanna waste his time.
440
00:20:36,360 --> 00:20:39,330
I don't wanna send him someone
that's gonna be causing trouble or
441
00:20:39,375 --> 00:20:40,650
just be a waste of an interview.
442
00:20:40,650 --> 00:20:40,920
Right.
443
00:20:40,920 --> 00:20:42,420
So you're definitely right that like.
444
00:20:42,960 --> 00:20:45,210
I had a bunch of people in my
program and, and you were the
445
00:20:45,210 --> 00:20:46,230
person I chose for that one.
446
00:20:46,230 --> 00:20:48,390
'cause I was like, I trust
Kadesha is ready for this.
447
00:20:48,390 --> 00:20:49,530
I think she would do a good job.
448
00:20:49,800 --> 00:20:51,180
I think she'd, she'd make me look good.
449
00:20:51,180 --> 00:20:52,380
She'd make the program look good.
450
00:20:52,650 --> 00:20:53,280
So you're definitely right.
451
00:20:53,280 --> 00:20:56,670
It's about networking and you have
to have substance behind it as well.
452
00:20:57,060 --> 00:21:00,420
Now going back to the coffee chats,
one thing that I think a lot of
453
00:21:00,420 --> 00:21:02,970
my students inside of the Data
Analytics Accelerator program.
454
00:21:03,630 --> 00:21:06,540
Are nervous about is, it kind
of seems like coffee chats can
455
00:21:06,540 --> 00:21:11,940
sometimes be one sided, like you're
just asking them to help you.
456
00:21:12,150 --> 00:21:14,490
So can you tell us a little bit
about your mindset that you went
457
00:21:14,490 --> 00:21:17,940
into these coffee chats and like what
type of like thing, like how would
458
00:21:17,940 --> 00:21:19,470
you even ask to do a coffee chat?
459
00:21:19,560 --> 00:21:20,640
Speaker 6: Yeah, that's a good one.
460
00:21:20,640 --> 00:21:24,570
So one thing that I've learned is that
people are more than willing to help you.
461
00:21:25,230 --> 00:21:26,940
But you do have to
present it in a good way.
462
00:21:26,940 --> 00:21:30,090
So I would try to find some
sort of like common ground that
463
00:21:30,090 --> 00:21:31,380
you have between that person.
464
00:21:31,470 --> 00:21:34,920
So like for example, when I was
at Cox Communications, it was, I
465
00:21:34,920 --> 00:21:39,300
would first reach out to people at
the company and say, Hey, I work
466
00:21:39,300 --> 00:21:40,920
here and I'd like to speak to you.
467
00:21:40,920 --> 00:21:42,450
You know, a general thing like that.
468
00:21:42,930 --> 00:21:46,680
If I would also go to like
my alma mater, so folks that
469
00:21:46,680 --> 00:21:48,250
went to Kennesaw State, I can.
470
00:21:48,854 --> 00:21:52,844
You know, you can, uh, filter it down
and find people who work, maybe like a
471
00:21:52,844 --> 00:21:56,594
data scientist or things like that and
say, Hey, I went to your alma mater, or
472
00:21:56,625 --> 00:21:58,215
I went to Kennesaw State just like you.
473
00:21:58,514 --> 00:21:59,385
I'd like to talk to you.
474
00:21:59,385 --> 00:22:00,885
This is a field that
I'm trying to get into.
475
00:22:00,885 --> 00:22:02,385
Try to find those common grounds.
476
00:22:02,385 --> 00:22:05,804
And you realize people do wanna
help you, but if you approach
477
00:22:05,804 --> 00:22:07,754
it in a sort of way that.
478
00:22:08,010 --> 00:22:09,840
You're not really
interested in that person.
479
00:22:10,260 --> 00:22:12,449
You're just interested in
them trying to get to a job.
480
00:22:12,570 --> 00:22:16,080
I feel like it's not gonna work, but if
you come across in a way where you're
481
00:22:16,080 --> 00:22:19,530
really interested in who they are,
what they did to get to where they're
482
00:22:19,530 --> 00:22:23,159
at, and try to find a common ground
between that person, you're gonna
483
00:22:23,159 --> 00:22:24,510
get a lot of yeses, in my opinion.
484
00:22:24,750 --> 00:22:25,050
It's hard.
485
00:22:25,050 --> 00:22:27,040
It's hard for people to
not say, to say no to that.
486
00:22:27,930 --> 00:22:28,379
Speaker 4: Yeah.
487
00:22:28,379 --> 00:22:30,450
I feel like most of the
time the answer's yes.
488
00:22:30,450 --> 00:22:34,139
Or an ignore or like a,
just like a no reply.
489
00:22:34,139 --> 00:22:37,740
So really that's all you have to
lose is you, if you ask for a coffee
490
00:22:37,740 --> 00:22:40,680
chat, you're either gonna get a
yes or you're gonna get an ignore.
491
00:22:40,680 --> 00:22:42,120
And the ignores don't really matter.
492
00:22:42,120 --> 00:22:42,340
And the yes.
493
00:22:42,900 --> 00:22:45,510
Can be, you know, almost life
changing at the end of the day.
494
00:22:45,870 --> 00:22:48,360
And I think you're right that people
are more willing to help than you
495
00:22:48,360 --> 00:22:51,690
think, especially if you have that,
no, if you went to the same school.
496
00:22:51,690 --> 00:22:54,870
If you are from the same hometown,
there's, I don't know, like our
497
00:22:54,870 --> 00:22:58,380
tribal instincts kick back in and
we're like, yes, this person belongs
498
00:22:58,380 --> 00:22:59,880
to the same group that I belong to.
499
00:22:59,970 --> 00:23:00,870
I want to help them the most.
500
00:23:00,870 --> 00:23:04,560
I can es, especially if you put
it in a way that it's like you're
501
00:23:04,560 --> 00:23:05,790
not even asking for anything.
502
00:23:05,790 --> 00:23:06,750
You just wanna hang out.
503
00:23:07,020 --> 00:23:10,800
But then what happens is, like you
said earlier, you're on their mind.
504
00:23:11,885 --> 00:23:13,895
When a job opening pops up, oh my gosh.
505
00:23:13,895 --> 00:23:15,575
I talked to Kadeisha a couple weeks ago.
506
00:23:15,815 --> 00:23:18,245
She'd be a great candidate,
uh, for this role.
507
00:23:18,245 --> 00:23:21,034
And you really, the name of the
game of that is just being top of
508
00:23:21,034 --> 00:23:22,534
mind for when opportunities pop up.
509
00:23:22,655 --> 00:23:23,915
Oh yeah, I remember this person.
510
00:23:23,915 --> 00:23:24,605
They'd be a good fit.
511
00:23:24,695 --> 00:23:25,504
Speaker 6: Absolutely.
512
00:23:25,565 --> 00:23:29,345
And I don't think people really
think about it that way or if they're
513
00:23:29,345 --> 00:23:33,965
scared about networking, but honestly
that's way better than trying to.
514
00:23:34,365 --> 00:23:39,795
Apply to 5,500 plus job applications
because the thing is you wanna put the
515
00:23:39,795 --> 00:23:44,685
ownership in your court, you wanna put
the power in your court, and if you just
516
00:23:44,685 --> 00:23:51,675
rely on, you know, applying to random
jobs or just LinkedIn or Indeed or
517
00:23:51,675 --> 00:23:56,060
whatever job platform site, honestly, like
the odds are not really in your favor.
518
00:23:57,150 --> 00:24:00,330
You know the power is not on your
side and you're really leaving your
519
00:24:00,330 --> 00:24:02,460
success up to just complete chance.
520
00:24:02,760 --> 00:24:06,660
So why not put the ball into your
court and put the power on your
521
00:24:06,660 --> 00:24:10,950
side and actually just really get
over your anxieties and meet people.
522
00:24:10,950 --> 00:24:12,630
It's really not that bad, honestly.
523
00:24:13,455 --> 00:24:14,925
Speaker 4: And I'm an introvert, so yeah,
524
00:24:14,925 --> 00:24:14,955
Speaker 6: e,
525
00:24:14,985 --> 00:24:15,915
Speaker 4: exactly.
526
00:24:15,975 --> 00:24:19,335
It's almost easier than applying for
all those jobs in the black hole.
527
00:24:19,425 --> 00:24:20,415
There you have it, folks.
528
00:24:20,445 --> 00:24:23,055
Coffee chats, joining
groups, sending messages.
529
00:24:23,205 --> 00:24:26,325
Hopefully you can see once again,
the important of networking
530
00:24:26,355 --> 00:24:28,125
in khas own data story.
531
00:24:28,185 --> 00:24:31,335
And not to sound like a broken record
here, but I try to include networking
532
00:24:31,335 --> 00:24:33,435
tips in my newsletter every single week.
533
00:24:33,435 --> 00:24:34,940
So once again, make
sure you're subscribed.
534
00:24:35,255 --> 00:24:36,965
The next story we have is Rachel.
535
00:24:36,965 --> 00:24:39,605
And Rachel was working
the dreaded night shift.
536
00:24:39,605 --> 00:24:44,045
She was not really loving having to work
all night and sleep all day, and she
537
00:24:44,045 --> 00:24:47,735
was listening to this podcast, the Data
Career Podcast, while she was working
538
00:24:47,825 --> 00:24:51,275
and just three months later, she was
able to successfully pivot into data
539
00:24:51,275 --> 00:24:54,995
analytics and you will not believe where
she was able to find her first day job.
540
00:24:55,025 --> 00:24:56,915
It's actually in a
place you'd never guess.
541
00:24:56,915 --> 00:24:57,695
Let's take a listen.
542
00:24:57,785 --> 00:25:02,075
You listen to this podcast Data Crew
podcast for a year, and then January
543
00:25:02,075 --> 00:25:04,805
21st you join the Accelerator program.
544
00:25:05,159 --> 00:25:11,129
And then on April 25th, I think just
95 days later, you have an offer
545
00:25:11,580 --> 00:25:15,959
for a business intelligence analyst
role at Optum Healthcare that is not
546
00:25:15,959 --> 00:25:17,939
the night shift and is fully remote.
547
00:25:18,270 --> 00:25:20,340
So let's, let's go through that journey.
548
00:25:20,340 --> 00:25:23,399
In those 95 days, what was, what
was the biggest difference for you?
549
00:25:23,399 --> 00:25:27,090
Like what, what changes did you
make in your life in those 95 days?
550
00:25:27,240 --> 00:25:30,810
Speaker 7: Well, even like leading up
to that, in the year where I listened to
551
00:25:30,810 --> 00:25:33,179
your podcast, I had a lot of free time.
552
00:25:34,020 --> 00:25:36,990
I knew I wanted to go back
to school something data.
553
00:25:36,990 --> 00:25:41,160
We used Power Bi Anheuser for
reports, showing some data
554
00:25:41,160 --> 00:25:42,870
every on a day-to-day basis.
555
00:25:43,230 --> 00:25:47,970
So I kind of looked for a podcast I could
listen to at night, and I found yours
556
00:25:48,120 --> 00:25:50,160
not even knowing that you ran a bootcamp.
557
00:25:50,670 --> 00:25:54,570
So in January I actually ran off Marathon.
558
00:25:55,200 --> 00:25:58,620
I on the same weekend decided
to sign up for your bootcamp.
559
00:25:59,220 --> 00:26:01,710
I think it was a very
big momentum step for me.
560
00:26:02,115 --> 00:26:05,295
I was just ready to get out of
where I was, and I actually had the
561
00:26:05,295 --> 00:26:09,675
opportunity to change from night shift
to afternoon shift for a quarter.
562
00:26:10,035 --> 00:26:15,735
So I had about three and a half months,
which I dedicated my spare time to working
563
00:26:15,735 --> 00:26:21,015
throughout the bootcamp, applying to
jobs, and I think just knowing I had
564
00:26:21,015 --> 00:26:26,295
that almost like extra sleep, gave me the
power to work through the bootcamp and
565
00:26:26,295 --> 00:26:28,185
really gave me the motivation to get a
566
00:26:28,185 --> 00:26:28,755
Speaker 4: new job.
567
00:26:29,685 --> 00:26:33,675
Um, and you are able to land
this job at Optum Healthcare as
568
00:26:33,675 --> 00:26:34,905
a business intelligence analyst.
569
00:26:35,235 --> 00:26:38,355
Um, first off, do you have
any business experience?
570
00:26:38,415 --> 00:26:40,335
Speaker 7: I mean, I worked
at a restaurant when I was
571
00:26:40,335 --> 00:26:42,014
in college selling pizza.
572
00:26:42,764 --> 00:26:46,545
That's probably about as much as
I have for customer experience,
573
00:26:46,725 --> 00:26:48,405
um, business background.
574
00:26:48,405 --> 00:26:49,004
So, no, not
575
00:26:49,004 --> 00:26:49,365
Speaker 4: really.
576
00:26:50,040 --> 00:26:52,380
So well that's impressive that
you're able to land this job.
577
00:26:52,380 --> 00:26:55,320
Did they like your biology background
at Optum or were they kinda
578
00:26:55,320 --> 00:26:56,190
like, ah, we don't really care?
579
00:26:56,310 --> 00:27:00,060
Speaker 7: I think it was more so like
other experience I experiences I had
580
00:27:00,060 --> 00:27:02,490
that tied into my biology background.
581
00:27:02,910 --> 00:27:09,990
I, at one point I wanted to be a nurse, so
I had some like care aide job experience.
582
00:27:10,200 --> 00:27:11,910
I worked in some different labs.
583
00:27:12,480 --> 00:27:15,180
I even worked in a
research department of a.
584
00:27:15,945 --> 00:27:17,385
Rehabilitation hospital.
585
00:27:17,385 --> 00:27:21,465
So I had some data experience with
that as well as like working in
586
00:27:21,465 --> 00:27:23,145
that type of patient population.
587
00:27:23,295 --> 00:27:28,785
So they liked that part as well
as the more like QA and technical
588
00:27:28,785 --> 00:27:30,705
skills that I learned at Anheuser.
589
00:27:30,735 --> 00:27:32,685
Speaker 4: I just think that's
really important to highlight that
590
00:27:32,685 --> 00:27:35,055
even though you maybe didn't have
business experience and you know,
591
00:27:35,055 --> 00:27:36,465
maybe you had a biology background.
592
00:27:36,990 --> 00:27:39,120
Your, your quality assurance analyst role.
593
00:27:39,120 --> 00:27:42,960
You know, you had some exposure to
bi, you had some exposure to KPIs
594
00:27:42,960 --> 00:27:43,995
and, and metrics stuff mm-hmm.
595
00:27:44,075 --> 00:27:44,700
And stuff like that.
596
00:27:44,820 --> 00:27:48,600
But with your background, even with the
bootcamp, you were applying for jobs and
597
00:27:48,600 --> 00:27:53,280
you were kind of struggling to, to land
interviews at first because you couldn't
598
00:27:53,280 --> 00:27:55,530
quite figure out the whole a TS, right?
599
00:27:55,530 --> 00:27:56,700
Like it was, it was tricky.
600
00:27:56,730 --> 00:27:57,120
Speaker 7: Right.
601
00:27:57,120 --> 00:28:00,875
So when I first started like
uploading my resumes to the a TS
602
00:28:00,880 --> 00:28:03,150
softwares, I had pretty good scores.
603
00:28:03,150 --> 00:28:05,550
And then when I started
nitpicking my resume.
604
00:28:06,345 --> 00:28:09,375
My scores actually went down,
which I thought was shocking,
605
00:28:09,585 --> 00:28:10,755
you know, using keywords.
606
00:28:10,755 --> 00:28:13,125
Some of them didn't even
pick up on the words.
607
00:28:13,515 --> 00:28:17,925
So that was kind of, you know, difficult
experience and I think that's where
608
00:28:17,925 --> 00:28:21,495
I really dug deep into the SPN method
that you talked so highly about
609
00:28:21,885 --> 00:28:25,515
the networking part, because that's
when I started reaching out to other
610
00:28:25,530 --> 00:28:31,335
people I knew in, you know, insurance
companies or other healthcare positions.
611
00:28:31,575 --> 00:28:32,685
And if they knew anyone.
612
00:28:33,044 --> 00:28:36,915
That had openings that they thought
I would be, you know, an asset to the
613
00:28:36,915 --> 00:28:37,304
Speaker 4: team.
614
00:28:37,334 --> 00:28:38,594
That's so cool.
615
00:28:38,594 --> 00:28:39,735
And I think it's so.
616
00:28:40,515 --> 00:28:41,685
It seems so simple.
617
00:28:41,715 --> 00:28:45,855
Like I talk about the SPN method all,
all the time, uh, right, that you
618
00:28:45,855 --> 00:28:47,475
need more than just skills on the day.
619
00:28:47,475 --> 00:28:49,515
Job skills are a part of
it, but it's just a third.
620
00:28:49,515 --> 00:28:51,465
You need the portfolio and
then you need the network.
621
00:28:51,795 --> 00:28:54,165
And it's so easy to be like,
yeah, I agree with that.
622
00:28:54,165 --> 00:28:55,035
I wanna follow that.
623
00:28:55,245 --> 00:28:59,535
But very few people ever actually
get very far on the p and even
624
00:28:59,535 --> 00:29:01,605
fewer get anywhere on, on the end.
625
00:29:01,905 --> 00:29:04,575
And so the, let's talk about
the end just a little bit here.
626
00:29:04,815 --> 00:29:07,785
So you're like, crap, I
can't get past these ATSs.
627
00:29:08,219 --> 00:29:11,129
I'm never gonna end a daily
job unless I can figure out
628
00:29:11,129 --> 00:29:12,330
how to get my foot in the door.
629
00:29:12,330 --> 00:29:14,639
And the easiest way to get my foot
in the door is if I know someone,
630
00:29:14,639 --> 00:29:17,280
if I the end part of the SPN method.
631
00:29:17,580 --> 00:29:19,020
And so where did you start looking?
632
00:29:19,020 --> 00:29:20,669
Just like friends and and family.
633
00:29:20,669 --> 00:29:23,129
Were you like going through your
phone, where you're going through
634
00:29:23,250 --> 00:29:25,139
LinkedIn or Facebook or something?
635
00:29:25,740 --> 00:29:29,699
Speaker 7: I actually did take a step
outta my comfort zone, like he pushed
636
00:29:29,699 --> 00:29:31,949
us to do, I, you know, looked in.
637
00:29:32,445 --> 00:29:34,485
I'm in Florida in the Jacksonville area.
638
00:29:34,485 --> 00:29:38,565
So I was looking in this area
just randomly cold messaging
639
00:29:38,565 --> 00:29:43,004
people that were in data roles,
asking them how they got there.
640
00:29:43,004 --> 00:29:47,715
Oh, do you have any like, you know,
advice for someone like me that wants
641
00:29:47,715 --> 00:29:52,155
to break into this field that might not
have the exact background but has the
642
00:29:52,155 --> 00:29:53,985
skills and is working towards the skills?
643
00:29:54,375 --> 00:29:59,385
And I actually ended up, a friend
from my last job had a friend who
644
00:29:59,385 --> 00:30:01,695
worked for United Healthcare and.
645
00:30:02,250 --> 00:30:03,060
Just to break it down.
646
00:30:03,060 --> 00:30:06,270
United Healthcare and them
are kind of like sisters.
647
00:30:06,540 --> 00:30:08,370
They're together in the
United Health Group.
648
00:30:08,730 --> 00:30:13,530
So he would, he let me know if there
was anything I saw job wise to let
649
00:30:13,530 --> 00:30:15,780
him know and he would recommend me.
650
00:30:15,840 --> 00:30:16,140
Speaker 4: Okay.
651
00:30:16,140 --> 00:30:20,220
So you basically messaged a
friend and you're like, Hey, do
652
00:30:20,220 --> 00:30:21,300
you know of any opportunities?
653
00:30:21,300 --> 00:30:22,170
How did you know this friend?
654
00:30:22,170 --> 00:30:24,780
Was this just like someone
from like high school or
655
00:30:24,780 --> 00:30:25,230
Speaker 7: something?
656
00:30:25,320 --> 00:30:28,890
Actually, a friend I go to church
with, one of her friends works at
657
00:30:28,890 --> 00:30:31,140
United Healthcare, so he kind of.
658
00:30:31,920 --> 00:30:36,150
Gave me the opportunity if I had, if
I saw any jobs that I liked to let
659
00:30:36,150 --> 00:30:39,750
him know and he could recommend me or
let me know a little more about that
660
00:30:39,750 --> 00:30:40,320
Speaker 4: position.
661
00:30:40,350 --> 00:30:45,960
That's so cool because actually the
way I, I landed my first day job
662
00:30:45,990 --> 00:30:47,730
was also through a friend at church.
663
00:30:47,970 --> 00:30:50,550
So if you're, if you're trying to
lend a date, a job, I guess you
664
00:30:50,550 --> 00:30:51,840
guys just gotta get to church.
665
00:30:51,840 --> 00:30:53,820
'cause that's, that's where
all the data jobs are.
666
00:30:54,120 --> 00:30:56,555
So you, you reach out to this
friend, you're like, hey.
667
00:30:57,165 --> 00:30:58,905
Just keep an eye open for me.
668
00:30:58,905 --> 00:31:01,965
And how long did it take for,
for them to send something back?
669
00:31:02,085 --> 00:31:05,985
Speaker 7: Well, I had seen a
job that looked interesting.
670
00:31:06,315 --> 00:31:10,485
It was kind of had a vague description,
so I sent it along his way and just
671
00:31:10,485 --> 00:31:11,895
let him know I was interested in it.
672
00:31:12,254 --> 00:31:17,535
And I applied and he actually had
emailed the manager directly and I got
673
00:31:17,535 --> 00:31:22,245
a call back from the recruiter maybe two
days later saying that, you know, they
674
00:31:22,635 --> 00:31:24,555
kind of got a raving review about me.
675
00:31:24,555 --> 00:31:25,335
They'd like to.
676
00:31:25,770 --> 00:31:27,180
Setting up an interview.
677
00:31:27,240 --> 00:31:30,660
It was kind of just a brief phone screen
at first, but I guess she liked what I
678
00:31:30,660 --> 00:31:33,000
had to say and it progressed from there.
679
00:31:33,060 --> 00:31:33,180
I
680
00:31:33,180 --> 00:31:37,290
Speaker 4: wanna just pause because you
basically went from, you're getting like.
681
00:31:38,415 --> 00:31:41,235
Like Fs on the a TS tracker, it
682
00:31:41,235 --> 00:31:43,065
Speaker 7: was, no, I'm not even kidding.
683
00:31:43,065 --> 00:31:45,135
It really must be a, a church thing.
684
00:31:47,325 --> 00:31:51,225
Speaker 4: You're getting FS and then all
of a sudden you're talking to a recruiter.
685
00:31:51,465 --> 00:31:55,785
You got, you got a direct line to
this hiring manager right away.
686
00:31:55,785 --> 00:32:01,155
And I think that just really goes to
show how valuable the, the end was.
687
00:32:01,155 --> 00:32:03,735
Because if, if you didn't do
that, if you didn't reach out
688
00:32:03,735 --> 00:32:05,055
to this person and from church.
689
00:32:05,475 --> 00:32:09,105
You know, you who knows if you'd have
a job right now in the data world.
690
00:32:09,110 --> 00:32:09,150
Right?
691
00:32:09,155 --> 00:32:09,345
Right.
692
00:32:09,345 --> 00:32:13,515
You might still be working the night
shift, but it's all because that end, I
693
00:32:13,635 --> 00:32:17,145
was it nerve wracking like sending that
first message to that friend and being
694
00:32:17,145 --> 00:32:18,675
like, Hey, can you help me out here?
695
00:32:19,215 --> 00:32:23,685
Speaker 7: It was, 'cause I knew he had
like a pretty high up role I, but I didn't
696
00:32:23,685 --> 00:32:28,905
wanna like, seem like I was using our
connection kind of per personal gain.
697
00:32:29,355 --> 00:32:33,825
But I've realized like since
kind of stepping out of.
698
00:32:34,725 --> 00:32:39,314
Comfortability that people really
wanna promote you and really
699
00:32:39,314 --> 00:32:41,925
wanna help you when you're driven.
700
00:32:42,225 --> 00:32:45,645
So that's what I've seen
gaining this connection.
701
00:32:45,645 --> 00:32:47,385
Now we have one-on-one meetings.
702
00:32:47,385 --> 00:32:51,524
He let me know if there's anyone I ever
want to talk to, just to network with.
703
00:32:51,524 --> 00:32:52,635
Like he'd set that up.
704
00:32:53,054 --> 00:32:59,264
So if anything, it was a great
choice on my end to kind of like put
705
00:32:59,264 --> 00:33:03,824
the ball in his court and send it
down, down court to other people.
706
00:33:04,245 --> 00:33:05,745
Speaker 4: I love that to send it.
707
00:33:05,774 --> 00:33:09,825
I freaking think that's the way to say it
right there is because like it is super
708
00:33:09,825 --> 00:33:13,784
nerve wracking to send these types of
messages, but as long as you're, like you
709
00:33:13,784 --> 00:33:16,995
said, like you're ambitious and you're,
you're a good person and you're not.
710
00:33:17,595 --> 00:33:21,315
Just trying to use them like you actually
value their friendship and you're just
711
00:33:21,315 --> 00:33:22,845
like, Hey friend, can you help me?
712
00:33:23,025 --> 00:33:23,925
I mean, think about it.
713
00:33:23,925 --> 00:33:27,255
If, if you, if your friend came to
you and said, Hey, can you help me?
714
00:33:27,255 --> 00:33:28,485
And you actually could help them.
715
00:33:28,725 --> 00:33:30,045
You'd want to help them, right?
716
00:33:30,045 --> 00:33:33,525
So I think we get in our head and be
like, wow, no one wants to help us.
717
00:33:33,525 --> 00:33:36,135
But it's like if someone came to
us and we have the opportunity to.
718
00:33:36,655 --> 00:33:37,615
We, we definitely would.
719
00:33:37,855 --> 00:33:40,284
And if we don't, we just say, you
know what, sorry, I can't help.
720
00:33:40,585 --> 00:33:43,555
And for, for us or whoever's
asking, they're in the same
721
00:33:43,555 --> 00:33:44,635
boat that they were earlier.
722
00:33:44,635 --> 00:33:47,245
So really there's not
much of a, of a lose.
723
00:33:47,245 --> 00:33:49,524
It's, it's a low risk,
high reward situation.
724
00:33:49,524 --> 00:33:53,605
So I'm proud of you for, thank you
for taking the initiative and doing
725
00:33:53,605 --> 00:33:56,965
it because there's a lot of people who
don't because they're, they have fear
726
00:33:56,965 --> 00:33:58,915
and you, you pushed it past the fear.
727
00:33:59,245 --> 00:34:01,465
I wanna talk about when you
did talk to that recruiter,
728
00:34:01,705 --> 00:34:03,055
what, what did the recruiter.
729
00:34:03,375 --> 00:34:06,765
Say, what were they like
interested in you for?
730
00:34:06,765 --> 00:34:07,575
Like what?
731
00:34:07,575 --> 00:34:08,594
What was that conversation like?
732
00:34:08,715 --> 00:34:13,304
Speaker 7: I will say like, she
asked me specific questions and I
733
00:34:13,304 --> 00:34:16,935
had mentioned I was in this bootcamp
and she was really intrigued by that.
734
00:34:17,445 --> 00:34:20,864
I don't know if it an up and coming
conversation in a lot of these type
735
00:34:20,864 --> 00:34:24,915
of recruitment calls, but she wanted
me to go into more detail and I
736
00:34:24,915 --> 00:34:26,715
was able to provide these projects.
737
00:34:26,715 --> 00:34:30,495
At this point, I don't think that I'd
gotten to the NBA Tableau project, but I
738
00:34:30,495 --> 00:34:32,505
did the education project at this point.
739
00:34:32,850 --> 00:34:36,989
So I got to talk on that a lot, and she
was from Massachusetts, so it kind of
740
00:34:36,989 --> 00:34:38,790
like made a further connection for her.
741
00:34:39,270 --> 00:34:40,889
So she got to see the project.
742
00:34:41,550 --> 00:34:44,969
And originally in my resume, she
didn't know that my projects were
743
00:34:44,969 --> 00:34:48,360
hyperlinked, so she was able to go
back to the manager and the director
744
00:34:48,360 --> 00:34:51,960
and let them know they could actually
see my work, not just read the little
745
00:34:51,960 --> 00:34:53,310
Speaker 4: description about the project.
746
00:34:53,370 --> 00:34:54,540
So that's super cool.
747
00:34:54,540 --> 00:34:56,670
I think that that one,
you had the projects.
748
00:34:56,925 --> 00:35:00,830
And, and that two, they didn't realize
it at first, and then they're like, Hey,
749
00:35:00,860 --> 00:35:02,270
do you have any experience with Tableau?
750
00:35:02,480 --> 00:35:05,660
And you're like, yeah, just click that,
that title right there, and then boom,
751
00:35:05,660 --> 00:35:07,910
your, your beautiful dashboard pops up.
752
00:35:07,910 --> 00:35:11,390
And I didn't know that the recruiter
from Massachusetts and that particular
753
00:35:11,390 --> 00:35:14,630
project that you did was on Massachusetts
data, but like the fact that like
754
00:35:14,840 --> 00:35:17,990
this recruiter could go through and
be like, oh, look at this county.
755
00:35:17,990 --> 00:35:21,950
Like I totally understand the data of
this county versus that county and, and
756
00:35:21,950 --> 00:35:23,960
kind of enjoy and have those aha moments.
757
00:35:24,255 --> 00:35:26,625
I think that's really what
the portfolio is all about.
758
00:35:26,625 --> 00:35:28,154
So you kind of nailed that.
759
00:35:28,154 --> 00:35:32,145
Being able to, to show them, Hey,
look, I, you know, I have a biology
760
00:35:32,145 --> 00:35:34,935
degree I've kind of worked in, in
the medical field and now I'm kind
761
00:35:34,935 --> 00:35:36,194
of working as quality assurance.
762
00:35:36,495 --> 00:35:40,335
I might not have the ideal data
background, but look, here's some,
763
00:35:40,605 --> 00:35:45,315
some evidence that I can actually do
what your job description says I can.
764
00:35:45,525 --> 00:35:48,525
That must have been kind of a
fun feeling to watch them look
765
00:35:48,525 --> 00:35:49,935
at your portfolio in real time.
766
00:35:49,995 --> 00:35:50,505
Speaker 7: It was.
767
00:35:50,505 --> 00:35:53,415
'cause like I know throughout
the bootcamp I was like.
768
00:35:53,925 --> 00:35:56,265
Like we said, a marathon note,
it was a straight sprint.
769
00:35:56,295 --> 00:35:59,565
Like I wanted to learn as much as
I could to build that portfolio
770
00:35:59,565 --> 00:36:01,155
to show it off to my network.
771
00:36:01,665 --> 00:36:03,345
So it was really rewarding.
772
00:36:03,795 --> 00:36:04,875
Speaker 4: Worth it in the end.
773
00:36:04,875 --> 00:36:05,145
Okay.
774
00:36:05,145 --> 00:36:06,165
Let's talk about the interview.
775
00:36:06,165 --> 00:36:09,165
So recruiter interview was was the
first phone call, is that right?
776
00:36:09,165 --> 00:36:09,255
Yes.
777
00:36:09,315 --> 00:36:10,365
And then what happened from there?
778
00:36:11,025 --> 00:36:16,335
Speaker 7: And then she set up an
interview with the director of the BI
779
00:36:16,335 --> 00:36:19,035
team and then the manager of the BI team.
780
00:36:19,755 --> 00:36:20,670
Speaker 4: And how did
those interviews go?
781
00:36:21,630 --> 00:36:22,830
Speaker 7: That one was great.
782
00:36:22,830 --> 00:36:24,300
The first interview was great.
783
00:36:24,570 --> 00:36:27,300
She said at the beginning like,
this is just a conversation.
784
00:36:27,300 --> 00:36:28,290
Don't be nervous.
785
00:36:28,680 --> 00:36:30,540
And that's all it felt.
786
00:36:30,540 --> 00:36:32,040
I didn't have any nerves.
787
00:36:32,070 --> 00:36:35,550
Usually I'm like kicking, playing
with my seat under the table,
788
00:36:35,550 --> 00:36:37,320
kind of like moving in my chair.
789
00:36:37,650 --> 00:36:39,510
But I didn't have any nerves at all.
790
00:36:39,660 --> 00:36:41,370
It felt so easy.
791
00:36:41,760 --> 00:36:44,400
And I could talk on the projects we did.
792
00:36:44,400 --> 00:36:47,500
I could talk on Tableau, I could
talk on some of the, you know.
793
00:36:48,120 --> 00:36:53,700
Metrics and KPIs I learned while I was at,
in my role at Anheuser and using Power bi.
794
00:36:54,120 --> 00:36:59,640
So they didn't get stuck on the fact that
I didn't have data analyst experience.
795
00:36:59,970 --> 00:37:05,670
They were more intrigued by how passionate
I was to learn more and grow in a role.
796
00:37:06,090 --> 00:37:06,930
So I think that's what
797
00:37:06,960 --> 00:37:08,070
Speaker 4: made me stick out to them.
798
00:37:08,940 --> 00:37:12,509
I really like that because in a lot
of interview situations it can feel
799
00:37:12,509 --> 00:37:16,360
like, oh wow, you haven't done any
data analyst work in the past, huh?
800
00:37:17,025 --> 00:37:17,565
Okay.
801
00:37:17,835 --> 00:37:20,055
But if you give them like so
much stuff where you're like,
802
00:37:20,355 --> 00:37:21,765
I'm super excited about this.
803
00:37:22,035 --> 00:37:25,635
I paid my own money and on my
own time I'm learning to do this.
804
00:37:25,965 --> 00:37:29,205
Here's, you know, seven of my
projects on different themes.
805
00:37:29,475 --> 00:37:30,405
Go look at it.
806
00:37:30,675 --> 00:37:33,795
I think you're giving them so much
stuff that they could think about
807
00:37:33,795 --> 00:37:35,025
and talk about and relate to.
808
00:37:35,330 --> 00:37:37,670
It kind of makes the interview
process a lot easier for
809
00:37:37,670 --> 00:37:40,010
them and you in, in that way.
810
00:37:40,010 --> 00:37:41,150
So I'm glad.
811
00:37:41,150 --> 00:37:42,290
I'm glad it went well.
812
00:37:42,440 --> 00:37:44,720
So that was, that was like the
first, the first interview, right?
813
00:37:44,840 --> 00:37:45,980
What about the final interview?
814
00:37:46,040 --> 00:37:46,730
Did that go well?
815
00:37:47,480 --> 00:37:51,560
Speaker 7: So the final interview was
about two weeks later and it was everyone
816
00:37:51,560 --> 00:37:58,400
on the team, so I believe six, five or
six people, and I was straight nerves.
817
00:37:58,520 --> 00:38:01,670
I was so nervous that whole time.
818
00:38:02,384 --> 00:38:04,815
I was like, I wonder if
they can see me fidgeting.
819
00:38:04,815 --> 00:38:10,725
It was that bad and I felt like I answered
a few questions really well, but then
820
00:38:10,725 --> 00:38:16,005
it was like I got caught on some word
and I just down downward spiral in my
821
00:38:16,005 --> 00:38:17,775
head the whole rest of the interview.
822
00:38:18,315 --> 00:38:23,384
But I remember like a few questions
that were just like probably to
823
00:38:23,384 --> 00:38:27,255
calm me down and kind of like joking
with them and I think that might've,
824
00:38:27,645 --> 00:38:29,235
you know, sparked some connection.
825
00:38:29,910 --> 00:38:33,480
But they had mentioned that I was
like one of the first people in the
826
00:38:33,480 --> 00:38:37,590
second round, so not to be like afraid
if I don't hear back for a while.
827
00:38:37,710 --> 00:38:41,040
Speaker 4: And so that was two weeks after
the initial interview, is that right?
828
00:38:41,520 --> 00:38:41,910
Right.
829
00:38:42,029 --> 00:38:43,230
Speaker 7: It might've
been a little longer.
830
00:38:43,259 --> 00:38:46,320
So I left that interview
thinking I did terrible.
831
00:38:46,740 --> 00:38:51,240
But even, even they reassured me a few
things, like, oh, none of them ever knew
832
00:38:51,240 --> 00:38:54,180
Tableau before they started in this role.
833
00:38:54,180 --> 00:38:56,520
So I felt like, okay, I
kind of have a leg up.
834
00:38:57,495 --> 00:39:00,254
When they said like, don't be
afraid, it might take a while.
835
00:39:00,254 --> 00:39:02,265
I was like, okay, I need to start looking.
836
00:39:02,265 --> 00:39:03,495
'cause I did terrible.
837
00:39:04,035 --> 00:39:10,064
But two days later, waking up at five
30, you know, night shift, waking up.
838
00:39:10,544 --> 00:39:14,384
I was actually going to a concert
that night, so I was gonna go into
839
00:39:14,384 --> 00:39:19,125
work after going to a concert and
I had an email saying the interview
840
00:39:19,125 --> 00:39:21,674
wanted to schedule a final offer call.
841
00:39:22,380 --> 00:39:25,080
Which was just crazy 'cause
I thought I bombed that.
842
00:39:25,260 --> 00:39:27,300
Speaker 4: That's a good feeling to have.
843
00:39:27,300 --> 00:39:29,790
So that was right before the, the concert.
844
00:39:30,150 --> 00:39:30,300
Speaker 7: Mm-hmm.
845
00:39:30,540 --> 00:39:32,070
And I called outta work that night.
846
00:39:34,140 --> 00:39:35,550
Speaker 4: That is, that is awesome.
847
00:39:35,610 --> 00:39:35,970
Okay.
848
00:39:36,210 --> 00:39:38,640
And then you scheduled
the, the final offer.
849
00:39:38,910 --> 00:39:39,780
Um, wow.
850
00:39:40,105 --> 00:39:42,835
Before we get into kind of,
first off, congratulations.
851
00:39:42,835 --> 00:39:43,675
That's amazing.
852
00:39:43,885 --> 00:39:47,005
And hopefully everyone listening's
clapping for you right now and
853
00:39:47,005 --> 00:39:49,615
jumping out of their, their
chairs because that's amazing.
854
00:39:49,855 --> 00:39:51,085
Those, those are my two tangents.
855
00:39:51,295 --> 00:39:52,345
Back to your offer letter.
856
00:39:52,525 --> 00:39:54,355
So you're excited you
get the offer letter.
857
00:39:54,355 --> 00:39:55,375
Are you, who are you calling?
858
00:39:55,435 --> 00:39:56,215
Who are you telling first?
859
00:39:56,245 --> 00:39:57,325
My fiance,
860
00:39:57,325 --> 00:40:00,265
Speaker 7: my parents, one
of my close friends at work.
861
00:40:01,935 --> 00:40:02,685
That's awesome.
862
00:40:02,955 --> 00:40:06,975
And then I reached out to you when I
needed some, some advice for negotiation
863
00:40:07,035 --> 00:40:09,585
Speaker 4: and that my friends,
is the power of networking and I
864
00:40:09,585 --> 00:40:11,325
guess the power of going to church.
865
00:40:11,384 --> 00:40:14,654
I challenge each of you guys to get
out today and to start networking.
866
00:40:14,654 --> 00:40:16,455
I promise it will change your life.
867
00:40:16,515 --> 00:40:19,875
I also challenge you to hit the subscribe
button and then watch this video next
868
00:40:19,875 --> 00:40:21,450
to get even more help on your data.
