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I'm a senior data analyst with 10+ years of experience and I'm breaking down exactly what I did, what tools I used, and what problems I solved across very different industries.
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⌚ TIMESTAMPS
00:00 – What nobody tells you about data analyst work
01:00 – Predicting refinery outcomes with math models
04:05 – When data analytics meets machine learning
07:00 – Finding needles in millions of log files
09:23 – How one analysis ended up driving marketing & sales
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Transcripts
Avery Smith-1: I'm a senior data analyst
with 10 plus years of experience.
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What did I do in those 10 years?
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00:00:05,565 --> 00:00:06,915
What tools did I use?
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What problems did I solve?
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That is the topic of today's episode,
and I'm gonna tell you everything
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so that way you know what to expect
as a data analyst in the future.
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I've had a really vast career where I've
worked for one of the biggest oil and
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gas companies in the world, and I've
also worked for a 10 person biotech
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startup that you've never heard of.
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Before, so let's get into it.
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By the way, if you're new here, my
name is Avery Smith and I try to
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share useful data content that will
help you start your data career.
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If that's of interest to you, you
gotta check out my newsletter.
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30,000 other aspiring data
analysts are already subscribed.
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Go to data career jumpstart.com/newsletter
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or find the link in the
show notes down below.
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So the first company I wanna
talk about is ExxonMobil.
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And what was it like being a data analyst
and a data scientist at ExxonMobil?
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Obviously this is one of the
biggest companies in the world.
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There's like 70,000 employees and
they do a lot of different things.
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Now, I worked in the downstream.
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Part of the business, which
basically means the refiners.
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These are the people that are taking oil
and turning it into gasoline essentially.
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And what do we do there as data analysts?
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Well, we tried to make a mathematical
model of every single part of the
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refinery, and I don't think this is,
you know, groundbreaking to those who
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are in the oil and gas business or
any sort of manufacturing business.
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If you can create what's called
like a digital twin or like a math
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twin of your process, you'll be able
to experiment with the math model
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instead of experimenting in real life.
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So you can be like, well, if I twisted
this temperature, or I changed this
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pressure, or we, you know, added
this new oil, what would change?
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Would we make more money?
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Would we make less money?
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What would go well?
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What would go poorly instead of actually
experimenting In real life, you can
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experiment with these simulations with
your data model, and that way you don't
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actually have to do it in real life.
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Now to create these models, there's lots
of different ways that you can do them.
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I'm not getting into the
nitty gritty of like.
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Modeling these types of things.
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But when you think model, the simplest
version that you can think of in
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your head is linear aggression.
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And if you're not familiar
with linear aggression, you
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learned it definitely in school.
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It's the simple thing
of Y equals MX plus B.
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That's the simplest form.
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So basically you have an input.
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An X.
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If based upon your input, can you
predict what the output is going to be?
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If it, you know is a linear relationship,
you'll be able to have the slope that's
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the m and some sort of a y intercept,
and basically guess what the output
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the Y is going to be based on the X.
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Now you can do that a
lot more complicated.
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You could do multivariate, linear
regression, which is like y equals.
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M1 X one plus M two X two plus X 3M three.
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Oh, it's so confusing.
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But my whole point here is like we
were doing these mathematical models,
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and the simplest form that you
can think of is linear aggression.
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So I created a lot of these
models as a data analyst.
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And I also used data analytics to try to
understand our simulation results better.
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So we'd actually run dozens,
hundreds, thousands of simulations
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trying, you know, different things.
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Well, what if this pressure went up by a
little bit, or this temperature went down?
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To actually look at a thousand
different results is really hard to do.
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So we used data analytics
to try to understand the
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results a little bit better.
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And a lot of this was done in a
Power BI dashboard, so I used a lot
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of Power BI dashboards right there.
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And to do the modeling.
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We actually did a lot in Excel, believe
it or not, and we did a lot in Python
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and we even used a more proprietary
software that you don't hear a whole lot.
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It's from sas.
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It's called Jump, JNP, to do our modeling.
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So those are the tools that we're using
at Axon, and that's the problem that
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we're trying to solve is basically,
hey, if we wanna make changes inside of
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our huge manufacturing system, can we
actually come up with a way to test it
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before testing it in real life so we can
kind of know and expect what to happen?
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I think that's common for,
you know, manufacturing.
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I think that's common for any sort of
like time series data you might have
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is if you can create a model, it's
useful for the company to be able
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to predict the future and be able to
figure out what's going to happen.
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A lot of the times this type of
analytics is called prescriptive
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analytics, where you're actually like
trying to not predict what's going
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to happen in the future, but trying
to decide if you make these changes.
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How will the system basically be affected?
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The next data job I wanna talk about was
when I was a data analyst at this nano
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biotech startup, like think 10 people.
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When I joined the company, this
company made really cool nano sensors.
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So think of it as almost like a game
boy, uh, game, like from the olden days,
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that's like the size of this little board.
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And on this board there was a bunch
of different sensors this, you
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know, chemistry company had built.
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And the sensors would basically react to
what was in the air and we would track.
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How their electricity basically,
or their, their amperage or their
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current, through these different
sensors would change when these
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different chemicals in the air hit it.
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So, for example, if you were holding
it in the air, you know, all the
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lines would be kind of stagnant.
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But for example, let's say you
brought an orange next to it, it
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would basically smell the orange.
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And each sensor would react differently
to that orange being nearby.
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And when you have, uh, an array of
these 12 different sensors, you can
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basically create the equivalent of
like a fingerprint, but for smells.
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So think of it as like the smelling device
that would basically take smell prints.
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My job as a data analyst there was to
actually look at the time series data.
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'cause we'd run these experiments where
you'd have like basically background
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noise for a certain amount of time
and then you'd introduce something
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like an orange for maybe 30 seconds
and then take the orange away.
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And we'd look at these time series and
we're trying to use these time series data
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to actually create these smell prints.
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And that's a very difficult thing to do.
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It actually most of the
time took machine learning.
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So once again, this is maybe a
more advanced data analyst role.
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'cause most data analyst roles.
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You're not really using machine learning.
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This type of machine learning is often
called classification, where you're
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basically trying to match data to a
certain category based off of its data.
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So for example, I could bring
an apple near it, right?
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And the sensors would react.
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Maybe they'd go all down, and if
I brought an orange next to it,
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maybe all the sensors would go up.
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And so you can come up with some sort of
an algorithm that would be like, okay,
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if the sensors go up, it's an apple.
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If they go down, it's an orange.
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Now that's really oversimplifying
it because apples and oranges,
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those are only two things that
exist in the universe, right?
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There's like so many
different things that exist.
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We were playing a little
bit bigger stakes.
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You can think of it when
you go to uh, TSA line and.
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And sometimes they, you know, swab you
and they're trying to see if you have
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like any drugs or any bombs on you.
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That was kind of the stakes that we were
playing with in some of our use cases.
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So I would take this data that oftentimes,
you know, was time series based.
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We usually had like 12 to 16 to
24 different sensors on there.
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And I would try to make these
smell prints using classification
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models in machine learning.
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Now, a lot of the time I was
doing this in Python python's.
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Great for doing things
in machine learning.
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There was even some simple
algorithms that I created that were.
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Based in Excel, but
they are pretty simple.
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The more complicated stuff.
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I was doing Python at the time.
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Also, just because we were doing
a lot of these experiments, SQL
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would've been really helpful.
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We weren't actually using SQL
as much as we should have.
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We really should have been using sql.
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Uh, looking back on it a little bit more.
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The third experience I wanna tell you
about was when I was doing my own,
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uh, data science consultancy firm,
and I got hired by a cybersecurity
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company to help them with a few things.
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So obviously we live in this digital age.
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Cybersecurity is really
important, so there's a lot of
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opportunity in cybersecurity.
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And the interesting thing
about cybersecurity is a
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lot of the data is like.
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Hidden in logs, because basically anything
you do online, anything you do on the
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internet gets logged one way or another.
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Like it's, it's in there.
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They're capturing everything, but when
you capture everything, you're kind
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of capturing nothing at the same time
because it's really hard to figure out
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what's the signal amongst so much noise.
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And so this company in particular
was basically getting a bunch of
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internet logs for companies in what
you can consider their workspaces.
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So for instance, all of their Microsoft
logs, all of their Google logs, if
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they're using Slack, their Slack logs,
maybe their employee customer history.
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Just think of like anything
a company might be interested
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in from a cybersecurity stand.
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We were just getting a bunch of the logs.
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Now in these logs, there's maybe
little needles in the haystack.
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There's maybe little gems
that can be pulled out.
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It requires a lot of analysis to
try to figure out what's in there.
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Just imagine you're getting
like a ton of hay and you have
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to find this little needle.
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And so my job was to go in there and try
to see if there was any needles, anything
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that was like really worth diving into
and investigating more, and also just
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summarizing everything that was happening.
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This is how many logins
you had on Google today.
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This is how many, you know,
logouts you had on Microsoft.
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You know, this is how many users
you had from these different states.
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Just like from these giant enterprise
organizations where they have thousands of
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employees and a bunch of things going on.
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Like how do you know
everything's going okay?
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Are you sure that like everyone
is where they say they are?
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Are you sure you don't have any intruders,
you know, people accessing stuff from
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a place that you probably shouldn't?
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Those types of things.
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So we were basically taking.
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These huge dumps of logs that weren't
really important, that weren't really
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interesting, and aggregating them and
trying to find the interesting things.
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And then also making sure
that nothing nefarious was
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going on to do that analysis.
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I was actually using all of Python, but I
could really choose what tool I wanted to.
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I just chose Python personally because
I'm very comfortable in Python.
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I'm, I'm decently good at Python, uh,
and I can do things quickly with Python.
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I probably couldn't have done
this as easily, like in Excel.
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You probably could have done similar
stuff in SQL if you wanted to.
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One thing I really like about Python
is it can do anything, maybe not
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extremely well, but it can do anything.
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Um, so like I was doing all my analysis.
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Uh, in Python and I was creating
data visualizations in Python.
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They even used a lot of the insights
I found, like in terms of aggregates.
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They basically like aggregated all of
their customers data and would publish
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like a, a yearly or, or biannual
report of like cybersecurity incidents.
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And so they were kind of like with graphs
that I was creating with some of these
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KPIs or metrics that I was monitoring.
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That way they could kind of inform
the cybersecurity, you know, fields
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all of their customers about like
what the trends and what we were
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seeing on a big picture standpoint.
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And that was actually really useful 'cause
people would start to like read that and
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be like, oh, I really like this company.
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I wanna work with them.
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And that would bring in new customers.
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So even though like I was doing
that analysis for individual
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customers at an individual level.
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That analysis actually ended up being
really useful for their marketing
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team as well to get more sales and
more customers in the pipeline.
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Now, I've actually worked for way
more than just these three companies.
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I've probably done work for
about 12, including like the Utah
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Jazz, Harley Davidson, and some
other really big names like MIT.
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If you want to hear more about
those, I'll be talking about
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them more in my newsletter.
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So you can
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slash newsletter, and
I'll be talking more.
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00:10:32,265 --> 00:10:35,324
About these experiences in the newsletter,
but if you want me to talk about it
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on the podcast or YouTube as well,
let me know in the comments down below
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and maybe I'll do some future episodes
on that if we get enough comments.
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As always, thanks for watching
and I'll see you in the next one.
