Episode 6 featured Christopher Bole, a data scientist for BlueCross BlueShield of Western New York. Chris talks about his career path starting as an analyst and moving to a data scientist. Listen to this episode to hear his advice for starting your career, the skills he thinks you need to be successful, and how his work has made improvements for his company and their clients.
(upbeat music) - This is Buffalo State Data Talk, the podcast where we introduce
you to how data is used and explore careers that involve data. In today's episode of
Buffalo State Data Talk,
Brian talks to Chris
Bole, a data scientist for Blue Cross Blue Shield
of Western New York. Chris started his career as an analyst and found his way to a
position as a data scientist where he enjoys anticipating issues
and finding solutions for problems, which save his company money and make improvements for their clients. Keep listening to hear Chris's advice on what skills he thinks you need
to be successful as a data scientist. (upbeat music) - Hello and welcome
back to another episode of Buffalo State Data Talk. I am your host, Brian Barrey,
and we appreciate you
joining us for Episode Six. Today, we'll be talking
to Christopher Bole, a data scientist at Blue Cross Blue Shield of Western New York. Thanks for joining us today, Chris.
- Thanks for having me, Brian. - So let's begin by having you tell us about what kind of work
Blue Cross Blue Shield of Western New York does. - Well in the simplest terms
we're a health insurance provider. The more complete answer is that we work with multiple stakeholders including the government,
physicians, hospital systems, and of course our members to
improve healthcare delivery,
patient outcomes, and
enhance the general wellness of our community while
maintaining affordable care. - All right, and as a data scientist for Blue Cross Blue
Shield of Western New York what are your main job responsibilities?
- So I'm primarily responsible for supporting new
initiatives, ongoing programs or vetting proposals. These may involve external entities such as a specific neurology practice
or a technology vendor
for medical wearables. And it always includes one or more of our internal departments such as actuarial, contracting,
or medical management. - And now that you walked me
through your responsibilities
could you tell me more
about what a typical day or week looks like for you? - Much of my time is really
broken into four main areas; meetings, where I spend time
with key business owners to clearly identify
objectives, critical metrics,
timelines, deliverables, and the like. Research which has dominated by reading and is often in unfamiliar subjects. There is the actual data
collection and analysis rarely a perfect is provided
to a data scientist.
So we have to have an understanding of our internal data systems. Also possess some ingenuity to identify appropriate
external data sources and then I will use different
programming languages
to build appropriate data sets. And from there, carry out my analysis. And lastly is building
reports that are suitable for the client and the main objective. Sometimes it may be as simple as a list,
list of people or list of doctors or list of facilities
requiring no further analysis. Often though, a report can
look like a research paper with an executive summary,
a background, methods, tables and graphs complete with findings
and discussion and recommendations. And data scientists on my team
are also known as innovators as many other people in the company. So a portion of my time is spent exploring potential opportunities
and bringing them to various
work groups to review. - All right, do you manage
or work with a team? Like do you typically spend
your time with the team or more time working independently or is it more of like a
balance between the two?
- My role is not supervisory, although I've been in that
position at other companies specifically I'm on the
decision sciences team. While collaboration is the
norm, the degree of integration depends on the types of projects.
Those people involved with
recurring or cyclical activities tend to work closely more often. - So do you have any tips for students on how to network with people or how to build their
connections in the industry?
- I do, that's a little bit
of a tough question for me because I'm not a big fan of
using the electronic universe for connecting with people,
sort of an old school guy I've been around for a while. And I don't feel that having
a profile on public sites
automatically generates relationships. My bias is more towards face-to-face interaction with people and as we all know,
that's difficult today. That being said, I think getting involved
with functional groups is an
ideal way to expand a network. For example, I found a Python
Meetup group here in Buffalo where professionals would get together and share actual business
problems and solutions. It was a great way to learn
about that specific language
but it also facilitated a connection between people and around a purpose. - Yeah, completely that the face-to-face just can't be substituted
with anything else. So it sounds like you worked
on many interesting topics.
What would you say is the
favorite part of your job? - Well, there are many. I would say that digging into a subject actually doing the legwork
of researching something. That's one of my favorite activities.
When I'm reading on a subject, whether it's something I'm familiar with or something brand new, I
know in the back of my mind for a fact that along the way, I'm not only getting basic
information to answer
what might be simple questions
around a business issue. Once that have already been proposed, I'm actually uncovering
potential solutions to problems that haven't even been thought of yet. And it's one of the ways
that I like to stay ahead of the game. - So like solving some real world problems and applying them. - Yes and actually anticipating problems before they even come up.
I can tell you for a fact that, when you are at your desk and a manager or a director or client comes to you and has a follow-up on
their original question and says, "Hey what about
this other avenue of inquiry?"
When you've already got
the answer for them? That's really impactful especially if part of that answer has recommendations
for addressing that issue or solving that potential problem that might've been presented
by that follow-up question.
- Yeah, I bet they love you for it. Now that you've told us about
your favorite part, Chris I wanna ask you about the opposite. Can you tell us about something that was the most
challenging part of your work
and how did you overcome it? - Hmm, well, it may sound trivial but some days are just a grind. I mean, you're working on
some code to build a dataset requiring a lot of complicated structuring
and you keep looking at the results but they aren't lining up
with what you need or expect. It's not even a debugging
issue, but a structural one. So you put on your favorite
grinding music of choice put your head down and
simply plow through it.
Whether by taking a break, working on something different for awhile, or sometimes talking
over the issue with one or more colleagues, you
eventually do get through it. But that can be one of the
most challenging things
in this kind of work. - Yeah, I agree with putting on some music and grinding through it, definitely. So our listeners would like to know more about your background.
What is your educational background and how you ended up where you are today? - Well, I two master's
degrees, one in mathematics and the other in business management and I completed a pre-doctoral
fellowship and cancer epidemiology. I actually entered the
field of research in a sense broadly speaking over two decades ago, even before the term data
scientist was even on the map. So I really entered the field.
My entrance was a non-event, so to speak. I've considered myself
a professional analyst and the titles have varied. The employers have changed, but the nature of my work
has always held my interest
as quite consistent. When I began working for Blue Cross, I had the title of
medical economics analyst. But over time I carved out a function that suited my interests
as well as the company's objectives. And ultimately I was able to present to one of the directors,
this idea of data scientist. This is a title, this is a
role they are compatible with a function that exists in a
lot of different companies
across many industries. It's easily translated. People understand what
you're talking about, and so our company developed
a track for data scientist. - And how you ended up
where you are today?
- I started working over again, over 25 years ago really,
I hate to date myself but I got into research
right out of school, right out of high school, essentially. And that type of work,
being an analyst, being a problem solver, that's always been a
strong interest of mine and I had good talent for it. Over time, of course,
the landscape changes and one thing led to
another and many people
who are data scientists sort of fall into that position by chance. If you were to ask me about
some of the background of other data scientists at the company, I can tell you it's interesting
because people have
degrees in biochemistry, anthropology, computer science, it's quite a wide array. Nowadays it's nice that there
are educational programs that do allow people to focus in
on this particular
profession, specifically. - So I'd like to go a
little bit more into that because students often wonder what skills or education they need to
join a certain career path. Can you tell me what you think
are the most important hard or soft skills when working as a data scientist? - Sure. Hands down. One of the most important things that aligns with success is
also the toughest to get.
And that is subject matter
knowledge and comprehension. If you're a fast learner
and a self-starter, that's not a huge gap to
impede your development. Basically when you start working, pretend you never leave
school, always keep reading
and listening and learning. Other capabilities that can
directly impact your success are sound problem solving
skills, creativity, flexibility around computer programming, and written and oral communication skills.
And as tentatively leaving
statistics either off the table or in the background. Frankly, if you're not a statistician there's not necessarily a huge benefit in trying to use those tools.
They're more likely to be misused and most business
challenges are actually met without having some kind
of degree in statistics. But definitely, the
computer science type skills being comfortable with
programming languages,
learning about databases. Those are definitely key skills and abilities to be able to
move forward in data science. - So about languages. Are there any specific
software technologies
or programming languages
such as Tableau, Excel, SAS that you often use in your work? - Yes, we use a variety of data management reporting packages for a variety of reasons.
There's no way for
someone to fully prepare nothing is standard across
businesses or industries and it's regularly changing. The best preparation is simply
using any of these enough that you're comfortable with
the concepts of their designs,
whether it's working with
pallet style applications like Alteryx or Tableau or the role columns
structures of Microsoft Excel or SQL server databases. In-house data storage
and retrieval systems will
probably never go away. But there's a constant pressure to get into the cloud, as they say which means that for some data scientists, they may need to focus
on developing skills
or at least an understanding
of the languages and platforms used by what I would say are
traditional developers. People that might program in Python or R or other similar other similar languages,
Java script, et cetera. I honestly get lost in
the jargon sometimes. I think this is one of the
bifurcations in data science that can lead towards specialization. And it's worth pointing out,
those who spend more time
with the nuts and bolts of storing and moving data versus those who are primarily analysts. Those skillsets can diverge quite a bit.
So as you ask about technology needs, if you're primarily going
to be a data scientist doing analytical work,
learning a bit of... Developing some skills and learning language like SAS or SPSS
that's going to benefit you
more than learning JavaScript. If you're on the other side
and you are in the cloud and using Amazon, AWS,
Amazon Web Services, or Google website, any
of those kinds of things you're probably going to move
away from things like SAS
or SPSS and focus more on
some of those other languages. - All right, well, switching
gears up a little bit and I'm not sure if
you can talk about this but could you talk about a conclusion that was made from data you analyzed
that you found interesting or
turned out to be beneficial to Blue Cross Blue Shield? - Well, as you're suspecting,
I'll have to refrain from any detailed answer
for a number of reasons. But I will say, I will say
that the work that I've done
analyzing and interpreting
all kinds of information, whether it was medical,
financial, operational, behavioral, to name a few
that work led directly to reductions or avoidance of millions of dollars in spending
as well as improvements
to patient care and outreach programs. Some examples of how
those were accomplished include building better metrics
or improving risk modeling or maybe by reverse engineering
of vendors' proposal and looking for weak spots and strengths
and approving those in the process. - Chris, you ended up doing a pre-doctoral fellowship in Buffalo, and now you work in Buffalo. How did you end up in Buffalo?
And why did you choose to stay here? - Well, I came to Buffalo from Rochester when I entered into the doctoral program for epidemiology at the
University of Buffalo. I planned to be here just long
enough to finish the program
and then move on to post-doc
work somewhere else. As it turned out, I decided
to leave the program without defending my dissertation. And 16 years later, I'm still here. I stayed mainly because
I found so many things
to enjoy about Western New York. - Yeah, there really are so
many things to enjoy here. Many of our listeners are younger. So as someone in a leadership role who may have been responsible for hiring
and mentoring new employees,
what advice would you give to someone who is interested
in working as a data scientist or who is just entered this field? - Well, I think structured
education can be really important and in some cases necessary.
But there's no substitute for experience. So don't be afraid to experiment. If you like problem solving in dynamic and sometimes demanding environments, you will probably find
this field very rewarding.
And I would say, read, read, read whether it's developments in technology, software or subject specific,
such as banking or telecom, insurance, keep educating your mind. - And finally, before we let you go,
is there anything else that you would like our listeners to know that we didn't cover today? - Sure, two things off the top of my head. Professionally, I would say
never stop asking questions
and definitely ask why. Because just with presumptions, people can get great looking results when they crunch numbers, but they don't actually answer
the questions they're asked.
And personally, well know
the jobs come and go. So maintaining interests and hobbies outside of your career
can help you stay positive as well as provide continuity
and growth in your life. Be kind to yourself and
remember that for every failure,
there is a future success. - Chris, thank you so
much for joining us today. And to all of our listeners, if you haven't already checked
out our previous podcasts they're available wherever
you listen to podcasts.
For more information
about starting your career as a data scientist, go to dataanalytics.buffalostate.edu. Don't forget to subscribe so that you get notifications
each time we release
a new episode of Buffalo State Data Talk. (upbeat music)
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