Cindy Clifford, a seasoned educator of 25 years, refused to let age or past career define her. She used her skills honed as a teacher and pivoted to data analytics! If you feel you're too old to pivot and become a data analyst, it's never too late-- dive into Cindy's story.

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⌚ TIMESTAMPS

00:00 - Introduction

01:26 - Burnout with teaching.

11:34 - Cindy's first data role.

13:04 - FindADataJob.com and PremiumDataJobs.com.

19:14 - Cindy's second data job.

30:10 - Advice for teachers who want to become a data analyst.

🔗 CONNECT WITH CINDY

🤝 LinkedIn: https://www.linkedin.com/in/cynthia-a-clifford/

🔗 CONNECT WITH AVERY

🎥 YouTube Channel: https://www.youtube.com/@averysmith

🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/

📸 Instagram: https://instagram.com/datacareerjumpstart

🎵 TikTok: https://www.tiktok.com/@verydata

💻 Website: https://www.datacareerjumpstart.com/

This is Cindy Clifford.

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And Cindy was a teacher and

educator for over 25 years until

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Cynthia Clifford: I reached a real

burnout stage with teaching and I knew

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I needed to do something different.

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Avery Smith: And honestly, can you

really blame her teaching is really,

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really hard in the first place.

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But Cindy was not only a teacher, she

was an international school teacher

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and endured some pretty crazy thing.

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Cynthia Clifford: I was stuck

in a military coup in Myanmar,

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and then I went to Vietnam and

I got stuck in Covid Lockdowns.

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I spent a year teaching online without

being able to leave my neighborhood.

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Avery Smith: Yeah, that's not fun at all.

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Being a teacher is hard,

but here's the truth.

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Teachers make great data analysts.

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In fact, most teachers are already kind

of analyzing data one way or another.

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Whether they realize it or not, teachers

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Cynthia Clifford: are constantly

evaluating and assessing the situation

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and our problem solving and data

analysis really is about problem

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solving and communicating the results

of the problems you've solved.

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Avery Smith: In this episode,

Cindy and I will explore her

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data career story and what helped

her leave a career of 25 years.

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Ultimately become a data analyst

at a company like Impossible Foods.

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Thank you so much for subscribing

to our show, and let's go ahead

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and dive into this episode.

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Alright, Cindy, you studied engineering

in college and then you had a 25 year

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career in teaching all over the world.

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What made you wanna become a data analyst?

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Cynthia Clifford: I wanted to become

a data analyst because, well, partly,

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and I know you've had other teachers

that in the program, I reached a real

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burnout stage with teaching and I knew

I needed to do something different,

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and I'd known that for a while, but it

really reached a height, as you said,

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I was teaching all over the world.

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I was stuck in a military coup in

Myanmar, and then I lost my job,

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and then I went to Vietnam and

I got stuck in Covid lockdowns.

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I spent a year teaching online without

being able to leave my neighborhood.

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None of that was good

for my mental health.

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And I came back to the US

after that summer and I said,

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alright, you gotta figure it out.

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You absolutely have to

figure out what you wanna do.

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And I spent the summer

informational interviewing.

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I met.

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Kind of everybody under the sun

made connections on LinkedIn,

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asked them if I could ask about

their job and what they did.

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I first thought I would want to do the

kind of things that a lot of teachers

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transition into, like, uh, instructional

design or learning and development

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in a corporate environment, and.

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Still realized that that wasn't the

direction I wanted to go, and I, you know,

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I taught high school math and statistics.

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I always, the math was

always my favorite subject.

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And data analysis started to

make, make a lot more sense.

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I reached out to a, a former

colleague who's still a friend.

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Who had already made that transition

and he's now a data scientist.

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And he and I talked a lot about

what I needed to learn and what

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some of the ways to learn were.

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And I decided I was gonna go for it.

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So my last year of teaching overseas in

Vietnam, I spent weekends and evenings.

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I started with the, the Google Data

Analytics certificate, and that confirmed

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that I wanted to go in that direction.

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But when I found you, I was really glad

because I knew that I wasn't really, it

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was like taking little quizzes and I'm,

I'm a good student, I can do that, but

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I knew that I wasn't really learning.

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To do things in a way that

was gonna help me find a job.

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So

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Avery Smith: it makes a lot of

sense 'cause my mom's a teacher.

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Being a teacher, I mean, obviously

you're making a difference in kids'

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lives and that's very meaningful and

we appreciate all of our teachers.

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But being a teacher kind of sucks

a lot of the time for many reasons.

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Like you said, long hours, low

pay, and it can be just like.

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Very stressful and, and

fatiguing, so it makes sense.

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You, you found something

in, in data analytics.

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You're like, okay, I'm good at

math, I'm good at statistics.

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Let's do, let's do this and find a

little bit more of a calmer career.

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Start off with the Google search.

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I had forgotten about that and I.

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And I like what you said, it would like

confirm that like, okay, this is something

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I wanna do moving forward, but didn't

like, feel like it prepared you for a job.

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Do you remember, I, this is

going off script here, but do

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you remember how you found me?

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Like this was, this was a while now

'cause you've been in your, in your

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career now for what, almost two years?

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I think so.

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Yeah.

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A

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Cynthia Clifford: hundred percent no.

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But I know that I had started

networking on LinkedIn and

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reaching out to various people and

making connections and comments.

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None of it's supernatural to me,

but I was already doing that and.

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Following people and finding people

who had made the transition to data

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who were formerly teachers, and

somewhere or other I came across your.

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One of those come and listen to

the, my program, you know, talks

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that you were running, what you

were saying made a lot of sense.

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You know, I am kind of cheap and I was

like, Hmm, is this like legitimate or is

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this, you know, one of these 'cause so

many sort of scammy things on LinkedIn.

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But I somehow, I trusted

and I'm glad I did.

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Avery Smith: Good.

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I'm, I'm glad you did as well.

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Like you said, you kind of spent,

uh, that last year of teaching

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ramping up to, for this transition.

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And I remember, I remember seeing

your, your comments in the community

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late nights, I guess for, for me

or or I, and for you, because, uh,

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of the time difference, we usually

have live calls like at 7:00 PM

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Eastern Time and for a while I,

where were you and what time was it?

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'cause you came to a

lot of our live calls.

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Cynthia Clifford: I wasn't

able to go to a lot of those.

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I was in Vietnam and it was like.

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Seven in the morning for me, but

I was already on my way to work.

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Avery Smith: I, I remember you coming

to a couple in, in the mornings,

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um, and you might be, well that's

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Cynthia Clifford: to then after

daylight savings or something.

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Ah, then it became six in the morning

and I could go for an hour or for 50

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minutes of it, and then I had to leave.

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Avery Smith: Perfect.

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You were very dedicated and you,

you did, uh, a lot of good research.

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Were you nervous to make

this transition though?

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Because you had been teaching for

over 25 years where you're like, can

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I really just reinvent myself again?

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Cynthia Clifford: I was definitely

nervous, but I was also fairly feeling

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fairly, like I just couldn't go on

teaching and I had decided I wanted

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to move back to the US and I did not

want to be a teacher in the US 'cause

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I thought that would've even been

worse than being a teacher overseas.

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Being a teacher overseas had been

really good for a long time until it,

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it wasn't, I didn't know exactly how

long it was gonna take you to find

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a job, but I had saved up transition

and felt like I had a bit of a buffer

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that if it also felt like, 'cause I

was already older, like it was sort

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of like, well, it's not now when like.

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Like, I, I have to do it.

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Like, so

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Avery Smith: I love that attitude

though, because I feel like a lot of

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people would just be like, ah, too late.

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You know?

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Um, but like, life's long and

you're also a very healthy person.

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We've talked in the past, uh, you

know, about, uh, you try to, try to eat

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healthy, try to exercise, stuff like that.

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I.

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Like life's long.

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Like we have an opportunity, you know, we,

we have to work, we have to go to work.

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It's a big part of our lives.

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Like, you know, you're spending

probably like around eight hours

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a day working everyone, right?

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And you want to be doing

something you enjoy.

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You don't wanna be miserable.

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Like if you're miserable now, like in

1, 2, 5, 10 years, like, what's going

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to change if you don't make a change?

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And, and even if like the best

time to plant a tree was 10

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years ago, the next best time.

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Is today.

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So I just wanna commend you for

being brave, because I think a lot

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of people wouldn't be brave and

be like, ah, oh, well I, I tried.

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Cynthia Clifford: Yeah.

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No, I, I, but I, I am, I think in a lot

of things, I have that attitude that

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it's never too late to try new things.

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I mean, I learned to cross country

ski this year and working from home

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in, in a cold climate like Vermont, I.

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Spend too much time indoors in the winter.

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So I decided this year that I

was gonna learn to do a pull up

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and put a pull-up bar outside my

right where my office door is.

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And every time I leave the room and to

come back, I have to practice a pull up.

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I can now do a pull up.

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Like, it's never too late to, to just try.

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Like, otherwise you might as well, like

you said, just curl up and it's done.

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Avery Smith: Be miserable.

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For those of you, uh, for everyone

listening, you can definitely

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tell what type of student, uh,

Cindy is because she is ferocious.

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Uh, you know, she's, she's willing

to do, she's dedicated, she's, um,

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consistent where she's like, even if

it's just one pull up, you know, I'm

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just gonna try to do that one pull up.

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Or if it's just a half, I'm

gonna do the half pull up.

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And that's how she was as a student

inside the accelerator program as well.

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And I'll have to say, you kind of had

to be, because you were transitioning

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from obviously not a tech field,

like teaching is not a tech field.

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And I'd almost argue that being

in education is almost like

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a non-corporate field, right?

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Like jobs aren't the same in the education

world as they are in other industries.

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Just because like it's,

things are just different.

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Like LinkedIn's not a thing and you

get a lot of jobs from your district

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or this and that, or like you're.

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I don't know who, you know, your

principal or whatever, plus like you

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weren't even in the us you hadn't even

been really in the US for a decade.

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And so, you know, you join

my program, you're like great

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Avery's, SPN method I'm in.

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And like the third part of the program,

33% of the program is networking.

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You're like, uh, I'm a teacher who's

been living overseas for a decade.

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My network is, uh,

maybe not the best ever.

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So I just, I just wanna give you

like some credit for one being like

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ferocious and battling through that.

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'cause once again, a lot of

people I think, would use

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that as an excuse and give up.

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But like how did you network with

like this education and international

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background that really maybe

wasn't super helpful for you?

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Cynthia Clifford: Well, I had

actually found a program before

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I found yours, which is how I

started getting into LinkedIn.

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I.

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That program has is something

that helps teachers transition out

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of teaching and there's a bunch

of lessons including networking.

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And I had been taking action

on that before I met you.

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I was joining data groups and I.

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Especially looking and searching

for people who were former teachers,

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and particularly if they were

former international teachers

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and looking for connections.

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And then I would just reach out to

them and ask them if, well, they

201

had done, made their transition

and started building a network.

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And the more.

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People that, you know, then you start

getting connected to more of them and it,

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it did start to grow and it, it grew a lot

more in the program 'cause other people

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would be connected to somebody and then

I would connect to them and then I would

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see people on their feet and I started

making comments and I actually really

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grew a pretty good network of people.

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But I didn't have, it was more

when I was looking for jobs, I

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didn't have people that I knew that

worked inside of a company, maybe

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with a different kind of role that

could help give me a, an internal

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referral or, or that, that's when I.

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It was more of a challenge.

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It wasn't so much a challenge networking

and meeting people as it was that I did.

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I didn't have INS any place.

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And I remember one of the, you were

trying to show us during the accelerator

216

program that we all knew people, and

you were like, I want you to take out

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your phone now, and I want you to look

at who you like Glass spoke to and.

218

Do you know what their's like, and people

were saying, oh, my cousins, or my, you

219

know, this, or my, and, and I'm like, oh,

I spoke to an independent farmer in Laia.

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Avery Smith: Not the most

data-centric role I would imagine.

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Cynthia Clifford: So that was where

it was more challenging, was in

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the job hunt part, not the meeting

people online and connecting.

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Avery Smith: So how did you, how did

you overcome the, the job hunt part?

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Or, or how did you end up landing

your, your first, uh, data role?

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Cynthia Clifford: I looked for lots of

kind of billboards and job sites that

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weren't necessarily just LinkedIn, like

I think I'm the one who told you about

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the tech Jobs for Good site, and I

followed lots of, I thought that my best

228

bet initially would probably be to get

with some sort of an education company

229

as a data analyst, so I was following.

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Ed tech, blogs of various kinds

and job postings through there,

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and I applied to a lot of jobs.

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Like I was more successful getting

interviews when I applied to jobs

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from some of these kind of less known.

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Venues.

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Um, I don't know if I ever really

got an interview from anything I

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applied for on LinkedIn, even if I

applied on the company website, but

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I'd found the listing on LinkedIn.

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I, you know, I just, I didn't

have the corporate background.

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I didn't have the connections, I didn't

have internal referrals, I had nothing.

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So I had to essentially called, call

everything and always sent cover

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letters that were very tailored.

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To the job.

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I always researched the company and I

probably applied to fewer places per week

244

than many of the students in the program.

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But I only applied to jobs

that I thought I was really

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legitimately, pretty qual like that.

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I, there was a reason why

somebody might look at me even

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with my limited experience.

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Avery Smith: That makes a lot of sense.

250

I think most people are on LinkedIn

and only looking at at LinkedIn jobs,

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which by the way, um, I don't know if

you have seen this, oh, you have seen

252

this, but I have find data job.com

253

now and premium data jobs, which are

trying to pull, help people find jobs that

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aren't necessarily listed on LinkedIn.

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So now you could have used those jobs

boards, but those didn't exist back then.

256

You're applying to jobs you think

you're a good fit for, you're looking

257

at job boards and job listings

that maybe other people aren't.

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And then.

259

You're, you're trying to stand out because

you're sending, you're sending cover

260

letters that are, that are quite tailored,

and then is that how, how you and

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Cynthia Clifford: following companies

that, that, that I, you know, ahead of

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time and commenting on, on that company's

posting and those things as well.

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Avery Smith: Okay.

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And how did you land your, your first job

with, uh, with Impossible, right, which is

265

basically they make the, the vegan meat.

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Cynthia Clifford: I found that job on

tech Jobs for Good, and I wrote a really

267

tailored cover letter because it was

very clear from the job description

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that cultural fit was really important.

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I made sure that they knew that I

tried impossible foods, that I, you

270

know, made ccad with the impossible

beet for my vegetarian sons.

271

That I really knew what I was, that,

that it's an important mission to

272

try to reduce some of the greenhouse

gases from animal production and

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that I'm behind that mission, and I

think that's why I got an interview.

274

Avery Smith: That's really cool that

you, you were really tying like,

275

you're like, Hey, I'm not just another,

you're not just another company to me.

276

I'm not just another candidate to you.

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I think this is a good culture fit.

278

We should also mention that like you,

you live in Vermont, it's not like the

279

biggest corporates tech hub of the United

States, so there's not a ton of data

280

jobs in Vermont, so you are also looking

for remote, which, which obviously makes

281

things, uh, a bit more, more competitive.

282

Um, so you apply to this,

this job as a remote job.

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Do you remember what the

interview process was like at all?

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Cynthia Clifford: I had a screening with

the the, with the recruiter who passed

285

me on to the hiring manager, and after I

met with her, I had four more interviews.

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With different people in either the

team or a team I might interact with.

287

They were all half an hour.

288

There were two back to back

and another two back to back.

289

So I met altogether with, besides

the recruiter, with five people.

290

And I do know that.

291

They speeded the process up a little

bit because they asked me early on if I

292

was close to an offer or I got an offer

from anybody else to let them know.

293

And I did get an offer from, and now from,

uh, an agency in Vermont, a state agency.

294

So I was able to sort of parlay that.

295

I mean, and it was legitimate.

296

I mean, I did get that offer, but.

297

It was, I was able to sort of put

pressure and move the process along.

298

Avery Smith: Okay.

299

And do you remember the

interview being hard?

300

Like were there difficult

technical questions?

301

Were they talking about stats or sequel?

302

Cynthia Clifford: No.

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All really kind of cultural fit

and behavioral questions and I.

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Avery Smith: I, I find a lot

of our students somehow get

305

internship or not internships.

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I find a lot of our students get

interviews that are, are more behavioral

307

and, and less technical, which, which

I think is, is quite interesting.

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Okay.

309

You're there for a bit.

310

And what type of tools, uh,

are you using on the job?

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Cynthia Clifford: Mostly Google

Sheets slash Excel and creating

312

templates of various kinds so that

I could take data that I would,

313

would access from outside databases.

314

I could take it and plunk it in

and it would automatically update.

315

I had, I'd created a bunch

of these sort of tools.

316

I had to prepare the weekly sales

and share report, which went to

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the executive leadership team.

318

That was all in PowerPoint, but I

would have to pull pictures out of

319

the, these templates that I had made.

320

So I used sheets, I used PowerPoint,

and, and then in the consumer

321

packaged goods industry, there

were a whole load of companies.

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Numerator, IRI, Nielsen, MPD, they

all point of sales data, if you think

323

about it, is a massive data set.

324

And so they kind of aggregate all

of this and they have their own

325

proprietary systems and you companies

pay subscriptions to access this data.

326

And I would have to do the data pulls.

327

I really did pretty much all the

data pulls and supported the sales.

328

Team and created these reports and

the logic of these systems was quite

329

SQL based, but it wasn't SQL because

there was an, you know, an overlay.

330

But I would have to, you know,

pick this and group by this

331

and wasn't highly intuitive.

332

It was actually pretty hard to

learn some of these, and there were

333

like maybe three or four different

systems I had to learn and one was

334

for food service and one was for.

335

Something else and one was just for Kroger

and both was, and each was different.

336

Avery Smith: I think that's important

to to note because it's not like, like

337

in the accelerator that we can cover,

you know, this, these types of tools.

338

And honestly like most jobs have some

sort of proprietary data software or

339

industry specific data software that like.

340

Really you don't even know

exists until you're there.

341

And even if you did know exists, you

probably really couldn't access it, uh,

342

unless you work for like a corporation.

343

So it's, it's like that's

exists at every job I

344

Cynthia Clifford: was interviewing.

345

They told me that I, part of the

job I would have to access IRI data.

346

So I looked up that thinking,

all right, well, I'll go see

347

what this is like before.

348

And to be even to get, be even

a researcher and get access

349

was over a thousand dollars.

350

So I was like, well, I guess

I'm not gonna access that.

351

Avery Smith: That's, that's how that goes.

352

Uh, that makes a lot of sense.

353

And you're wise for like, trying to look

it up beforehand and, and be prepared.

354

That was, that's really cool.

355

Okay, pause for a second.

356

Uh, I didn't really think through how

we wanna transition to your second job.

357

Uh.

358

I can say you're just there for a while

and then like you ended up getting

359

into, and they had had a reduction in

360

Cynthia Clifford: force and they moved.

361

Um, and well that what, what they

actually did was they reclassified

362

all these jobs as hybrid honest truth.

363

They did that because they

wanted people to quit, but Yes.

364

Um, because they had then ended up

with a big layoff shortly after that.

365

So I think we can just sort

of say there was sort of.

366

They, they transitioned jobs and

there was a reduction in force.

367

Avery Smith: Okay, so you're at

Impossible Foods for a while.

368

And then they ended up kind of

reclassifying a lot of jobs to, instead

369

of being remotes, to be hybrid and, uh,

their, their offices are not in Vermont.

370

And so you ended up, uh, not being

able to work at them and any further.

371

And then you had to

find, uh, a new data job.

372

How did you find the second data job?

373

Cynthia Clifford: Well, I actually,

this time I had several internal

374

referrals for things within the

consumer packaged goods industry.

375

So I was pursuing those.

376

I also was pursuing things I'd

found on LinkedIn or on your

377

job boards or, and I gone.

378

Actually the final round four

times and didn't get the job.

379

It was exhausting.

380

I mean, you know, done the project,

done a panel presentation, like

381

all sorts of stuff for several

jobs and was feeling pretty down.

382

And I'm not, and somebody I know

from LinkedIn and I think from this

383

program, but, uh, okay, so someone

from the program who I'd connected

384

with and we've had coffee chats with.

385

And continued to keep in contact

with, 'cause I always appreciate

386

her thoughtful comments on LinkedIn.

387

I had chatted with her, uh, because

she was looking for a new, a new role

388

or had just gone through the process

of looking for a new role and I let

389

her know with the position I was in,

and she actually said to me, I just

390

interviewed with a company and I.

391

They offered me a job

and I'm not taking it.

392

And she said, not because it wasn't

a good job or a good company,

393

but she had personal reasons for

why it wasn't the best fit for

394

her circumstances at the time.

395

And she said, if you'd like,

I'll, I'll write to the hiring

396

manager and recommend you.

397

So even though she had turned this job

down, she wrote to the hiring manager

398

and or to the, the recruiter and told

him that she thought I would be a

399

great fit and I ended up meeting with

him without actually even applying.

400

And he then set me up to interview with

the hiring managers also before I'd ever

401

filled out an application on the site.

402

And.

403

Because I know that after, after

meeting with the hiring managers, the

404

recruiters said, you know, we need to

have you fill out this application.

405

And she was great because she had

given me a little bit of heads up

406

about the sorts of questions they were

gonna ask me in the interview as well.

407

So I was able to be very prepared.

408

The interview was.

409

With the hiring managers.

410

There were two of them.

411

It was a, it was a good interview.

412

They were both really thoughtful.

413

It was clear that they

had a set of questions.

414

They were growing the team substantially.

415

A, a year before I joined, this

particular team had maybe five or six,

416

maybe seven people, and now we're 20

and they'd hired, I was one of the

417

last of this big explosion of hires.

418

The.

419

Questions were a mix of, I wouldn't say

highly technical, but they did ask what

420

I, I mean, this is in the energy industry.

421

They asked, you know, what I knew

about how power was generated.

422

They asked if.

423

They asked questions about what was

the most complex sorts of things

424

I've ever done with Excel, but they

also asked behavioral questions.

425

Avery Smith: Well, what's cool is,

you know, you have been working as an

426

international teacher for, for a while,

but you studied engineering in school

427

and you even had an engineering job, you

know, out of college for a little bit.

428

So I'm sure that like not only having

this awesome, basically internal

429

reference to the hiring manager.

430

Also being like, Hey, look, I

understand engineering principles.

431

I think that probably sets you

apart compared to most analysts.

432

Cynthia Clifford: Oh, for sure.

433

Because when they asked me, you know, what

I knew about how energy was generated,

434

you know, you know, I was like, well, I.

435

I just spew off an answer like, well,

there's lots and lots of ways of, you

436

know, getting, you know, converting

sort of potential energy to kinetic

437

energy and getting that turbine moving

and getting, you know, and like I, you

438

know, I went on and thought it on, I

think, and, and it's been really useful

439

in my work there to have that sort

of understanding all of the analysts.

440

Take Workday courses all on

things like HVAC systems and,

441

and when I was an engineer, you

were chemical, I was mechanical

442

and thermodynamics was actually.

443

My best subject engineering job

I had when I was an engineer

444

was in energy conservation.

445

So even though it was quite a

while ago, those fundamentals are

446

in there and it's helpful now.

447

Avery Smith: Very cool.

448

I wanted, I wanted to ask earlier, like,

you know, even though you were a teacher.

449

Did you find that you had transfer

transferable skills into analytics,

450

and obviously sounds like in this

case your, your engineering background

451

stuff was, was transferable.

452

Were some of your teacher

skills transferable as well?

453

Cynthia Clifford: Oh, for sure.

454

I think that, I mean,

in a variety of ways.

455

In my current role we are,

we do a lot with statistics.

456

We look at the statistics of models,

are these appropriate models?

457

Are is the, are the residuals

normally distributed?

458

That sort of thing.

459

And having taught higher level math

and AP statistics, I've been able

460

to actually contribute to my team.

461

By creating, we have team weekly

team meetings that are team trainings

462

where people will present things and

I presented on, oh, here's the Durbin

463

Watson statistic and auto correlation,

and what does it really mean?

464

And used really simple examples that.

465

That aren't necessarily embedded in

the energy context, but are maybe

466

embedded in ice cream shops and beaches.

467

Everybody can understand and people have

said that they've been really helpful.

468

I, knowing the statistics has certainly

been transferable and, and, and math

469

modeling, I mean, understanding variables.

470

I, you know, I was the calculus

lady, but other skills that all

471

teachers have are really transferable.

472

Teachers can learn new things.

473

When you're a teacher, you.

474

You get thrown into, you know, they'll

be like, oh, we have a new software that

475

we're gonna use for, you know, great.

476

And they'll bring one person in and

do a two hour point and click and

477

then they'll be like, off you go.

478

And teachers figure it out.

479

'cause they have to, I've been surprised

in the corporate world actually,

480

how much time they give you to.

481

Learn things.

482

'cause when you're a teacher,

they don't give you that.

483

I think things like knowing how to

do a presentation in, in Impossible

484

Foods, I had to make PowerPoints.

485

Like I actually, at one point, I, I

looked at the PowerPoint and I was like,

486

you know, we just come out with these

new company color branding and like,

487

is is there any chance I could like

redo the template for the PowerPoint?

488

So it's very cohesive, like, and what I

made then ended up saw it showing up in.

489

People much higher than me kind of taking

my templates and using them because

490

I, I know how to make a power one.

491

Avery Smith: There's, there's all

sorts of different ways that teachers

492

can, you know, transferable skills.

493

Even, even when you said earlier when

you were talking about some of the

494

statistics and, you know, maybe not in

energy, but like in ice groups and stuff

495

like that, teachers are, are good at

explaining things and really like what

496

you're actually doing as a data analyst.

497

A lot of the time is just telling business

people or higher ups what's happening in

498

the business from a numbers perspective.

499

And so as a teacher, you're, you're,

you've been trained to communicate

500

clearly, whether it's in a PowerPoint

or, or orally to say what's going on.

501

Uh, and like you said, also,

teachers are fantastic students.

502

And like you said, at Impossible Foods,

you had to learn this like proprietary

503

database system that like you couldn't

really learn on your own beforehand.

504

At your, your current company.

505

We haven't talked about it, but

you use this software called jump.

506

JMPI really like jump as well,

but it's not like something that's

507

really, it's not super common.

508

It's, it's an awesome tool, but it's not

super common and it's quite expensive.

509

Um, if you try to get like a

license on your own, it's gonna

510

cost you about $2,000 a year.

511

So it's not like something you, no

one really learns, jump on their

512

own and then gets into a job.

513

You always learn jump.

514

On the job, and that's something

that teachers are gonna excel at.

515

They're gonna be great.

516

And, uh, to be honest, especially with

how AI's going right now, like we're

517

gonna have to keep learning new things

year after year after year as a data

518

analyst for the next two or three decades.

519

Cynthia Clifford: Well, I use AI

a lot of times in, in my role when

520

I'm, I'm doing some of the Excel

based work and I know I wanna maybe.

521

Pull something from this tab over to this

one and, and aggregate it by the week.

522

And, but when I, if I have blanks, I

don't want them to show up as zeros.

523

I want them to show up as nas, then I will

put the appropriate information, describe

524

the situation and put that into ai.

525

'cause you, you can't obviously,

you know, company spreadsheet, you

526

know, with chat GPT, but, but I will

put in the relevant information and.

527

I ask for the, the code, and it's really

good at giving me very succinct ways

528

to do some of the things I need to do.

529

Avery Smith: I, I love that.

530

It's just AI is not replacing us.

531

It's just helping us work faster.

532

Um, I think that's really cool.

533

Has anything, has anything really

surprised you as a data analyst?

534

Like maybe something you didn't realize

that, that the job would be like?

535

I.

536

Cynthia Clifford: Well, I would say

that my first role, I was surprised by

537

a lot of things, but a lot of that was

more just the way that corporate works.

538

Coming from a teaching background, I,

things are so different in teaching.

539

They want you to get something done

fast and it might not be the most

540

perfect version of something, but

if they say they want this, they,

541

well, they'll get something and

they'll get it when they need it.

542

I found that I had that

mentality and would be like,

543

well, did you proofread this?

544

Did you, I mean, like of course I

proofread it, but did you check this?

545

Did you run it by three or four

other people and get their feedback?

546

Did you do like for things that

were supposed to be rushed and.

547

Could end up being, we're gonna

roll this new dashboard out,

548

it's gonna take two months.

549

And teaching it would be like, well,

here it is, and like, you know, start

550

playing with it and figure out what

you can, if there's problems, let me

551

know if there's problems, let me know.

552

Be an issue.

553

In teaching, it would be part of

the process of how things work.

554

And it seems like in the corporate

world, it's all a lot slower.

555

But it has to be right.

556

Like they're not iterating

constantly on the fly.

557

You're supposed to do all these

iterations and then say, here,

558

Avery Smith: it's, it's, it's definitely

a different world than, than teaching.

559

Uh, for sure.

560

What advice would you give to teachers

who want to become data analysts?

561

Cynthia Clifford: The teachers are

constantly evaluating and, and assessing

562

the situation and our problem solving

and data analysis really is about problem

563

solving and communicating the results

of the problems you've solved or, you

564

know, every, like you said before,

if, if, if they're sales data you're

565

trying to explain to an executive, not,

you don't need to explain that the.

566

Sales went up, or sales went down.

567

That's a, like a concrete number,

but you're trying to dig into

568

why and what other drivers are

there that made that happen?

569

Or in my current role, which are the

variables that are gonna best explain,

570

uh, best represent, allow us to create

a model that will describe a company's.

571

And there might be tons of different

variables, but we're trying to

572

come up with the ones like a really

simple model that will still explain

573

really clearly and teachers do the

same thought process all the time.

574

Why is Joey not understanding?

575

This concept?

576

What is going on?

577

Is there a piece that's missing?

578

Is there like all that back

thinking and the, Hmm, let me think.

579

Let me take a look.

580

Does he know how to do this?

581

Does he know how to do this?

582

Does he know how to do this?

583

Oh, and then he doesn't

know how to do that.

584

So somewhere there's this connection that

Joey's not making or Johnny's not making.

585

Teachers do that all the time, and

they do it for rooms full of kids.

586

And they finish the day and they ruminate

over what went well and what didn't

587

go well and why you're just applying

that same skillset, that same sort

588

of thought process to a new context.

589

Avery Smith: It's problem solving at

the end of the day, and teachers have

590

always been good problem solvers.

591

Uh, Cindy, you're one of the

best problem solvers I know.

592

Uh, and I'm sure, uh, your current

company is super lucky to have

593

you, and I was lucky to have you.

594

As a student in, in the Accelerator.

595

Thanks so much for coming on the

show and, uh, sharing your story.

596

Cynthia Clifford: No, it was my pleasure.

597

It was really good to catch up.

598

Avery, you were wonderful

to me and continue to be

599

Avery Smith: good.

600

I'm glad I.