>>ALLIE: My dad had also worked in Silicon Valley.
And so one day he called me and he was like,
I know what you're going to do with your life.
And I was like, dad, I'm trying to sleep.
And he was like, big data, I said excuse me.
But I've also just been fulfilling that prophecy, I guess.
>>CELIA: And loving it too.
>>ALLIE: Yeah, yeah.
>>CELIA: So this is gonna be a very casual,
will kinda go over, like your experience at Michigan
and then kinda what you've been doing
in the past couple of years since you graduated,
but we can start off with some introductions.
So I'm Celia, I'm a rising senior and IOE.
I have loved being a part of the college engineering
specifically through Phi Sigma Rho, engineering sorority,
as well as through an elementary engineering partnerships,
which is like an organization that teaches
elementary students about engineering.
But I'm very excited to hear kind of like your perspective
and your opinion on how college influenced
those moves for you.
So if you just wanna introduce yourself a little bit
and then we can move into like some questions.
>>CELIA: So my name is Allie Cell.
I was a 2018 graduate in data science engineering.
And so like one of the first classes
in data science engineering,
and I currently work at Shift in data science.
My title is data analyst right now
and we buy and sell used cars,
which I think is like actually really interesting,
I'm sure we'll talk about that later.
And when I was at Michigan,
I was also really involved in Phi Sigma Rho
and engineering sorority, the Michigan data science team,
the club running team MRun.
And then I also did a multidisciplinary design program.
>>CELIA: Can you describe a little bit more
what the company does and then what your role there is?
>>ALLIE: Yeah, yeah, so we buy and sell used cars.
And so what that looks like is that people will,
or people who wanna sell their car
will like input information about their car online,
and then we'll provide an instant price for them.
And so that's our biggest use case for machine learning
at our company.
And it's, pretty hard to do an interesting
because every single used car is unique,
whether it's different mileage or different features,
and also people don't buy or sell used cars that often.
And so data for it is a little bit sparse
compared to some other fields of data science.
And then one of the biggest projects that I've worked on
is looking at where Shift historically
has cleared similar cars
to know how to like pay sellers more for that car,
if it's more desirable or pay them less,
if we've had trouble selling similar cars before.
I also am in like a meeting with like the CEO and COO,
like every Wednesday,
which is where I do a lot of analytics about like,
should we be offering sellers more money or not enough cars?
Like, are we acquiring enough cars?
Are we selling enough cars?
Should we be lowering our this prices online?
Asking a lot of those questions.
So it's been super interesting.
>>CELIA: What are kind of aspects of your job
that are your favorite versus your least favorite
kind of tasks or things to do or?
>>ALLIE: Yeah, I think least favorite
would be that especially working at a startup
there's, it's not always clear how data science
is supposed to interact with other like project teams
or engineering teams.
And so we're not always like involved
in each steps of planning each project.
And then my favorite part,
being able to work at a small company
and not only like work on
and like we have machine learning projects
with like a year of experience,
but also be able to like scope them out
and identify use cases from working
in like in the sphere and being like the domain area expert
has been super cool.
And I'm really, really lucky to have that experience.
>>CELIA: Do you have any kind of general advice
or pro tips or like guidance for students
that maybe are like thinking about studying data science
or choosing engineering in general?
>>ALLIE: I love data science, so I would recommend it.
I think, I liked the idea of computer science,
but I never felt like I would be the best at coding.
And like now I know after being in industry, like, okay,
that's not everything that majoring in computer science is
and software engineers do some things
that aren't just coding,
but I do feel like I really get to
utilize more skill sets.
And I think that, that really suits me very well.
>>CELIA: Why, what makes you love data science?
Why do you love data science so much?
>>ALLIE: I think it started a little bit
with like when there was like a survey phase
where everybody would send out quizzes
when I was in middle school.
And I was so into that
and I would like send out quizzes and I was,
I would always try to make sense of the results,
even though I had like five friends.
So there was nothing significant.
And so I kind of don't know how to describe it, but,
and I think like mapping is cool.
Like visualization is cool,
like solving really hard problems
that are complex is really cool
and like having the technical skills to do that.
So I think there are a lot of different aspects
that one could love,
but I don't know how to explain why I do maybe.
>>CELIA: You feel like this is the right path,
this was the right path for you.
This is fulfilling you you're satisfied with
your experience through college and your career.
If you had to change anything,
would you, or would you leave it the way that it was?
>>ALLIE: I in college, I definitely didn't really
prioritize classes over my extracurricular involvement,
which had some impacts that were good
and some that were bad to my GPA and what not.
And so maybe trying to like balance that better
would have been good if I would have done it,
but I am lucky to be where I am now.
I think like the Michigan data science team was definitely
my most like helpful professionally
experience at Michigan but.
>>CELIA: Why is that?
>>ALLIE: Wow, I had a little bit of experience
in like data analysis through an internship,
after my sophomore year,
but going into my junior year, I like,
I been majoring in data science for like a year and a half,
but without actually any
like really big data science projects.
And I made my own little, like,
I learned how to like map data basically at my internship.
And so I took that and me in my own little like
mapping off campus housing prices at Michigan
because I like loved housing.
I was housing chair for our sorority
and I also really liked visualizing data.
And so when I went into this intro meeting
for the machine data science team, I was pretty intimidated
because a lot like over half
of the people were grad students.
They were like, oh, like,
if you've done a data science project or something,
then you should be able to actually
like ready to go with us and do other data science projects.
And I was like, oh, nice,
like I just got that a little one in.
And so then I went on to like lead a project,
working with like the City of Detroit
and predicting like compliance.
And they're like first authored a paper
and like led a team there and presented
at a few different panels and stuff at Michigan and Beyond.
But I think like the,
one of the coolest things I learned there
is that just because you can like build a model
that predicts something really well, doesn't like,
that's definitely not all of what data science is
because we built this model, it's pretty good,
but especially in terms of like how a city,
like what models are useful for like the City of Detroit,
we predicted like people who wouldn't pay fines,
but that doesn't mean
that you're not gonna give fines to everybody
or like, and I think trying to think of how,
actually like being really thinking through the use cases
for any modeling is just as important as the modeling
more important.
So I don't know, that was like one experience
on the Michigan data science team.
We also did like Kaggle competitions,
which is like a whole competitive sphere
of like the data science community.
And yeah, I don't know, there's a lot of good stuff there.
>>CELIA: Are there any other experiences you had
in your undergraduate career
that you feel like helped inform
either positively or negatively like where you are now?
>>ALLIE: I think like just like being able
to have a major that was data science engineering
was super helpful
because I, especially when I go to like events out here,
a lot of them are for like people
transitioning into data science
or like women transitioning into data science
after being in a different career.
And that is really hard to do without like,
without either like a master's in say all these
masters in data science programs
or like real project experience.
And so, which is hard to get if you're working full time
at a different job.
And so, having the name data science on the resume
definitely is really helpful.
>>CELIA: Yeah, that's really cool.
Thank you so much for joining us,
I really appreciate you being willing to like share
all your experiences and go into detail
about what you're doing now.
And it's really awesome to see you also.
>>ALLIE: Yeah, yeah, you too no problem thanks.
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