Data are becoming the new raw material of business
The Economist


Leaving Tenure for Data Science – Alumni Spotlight: Bartley Richardson

At The Data Incubator we run a free seven week data science fellowship to help our Fellows land industry jobs.  We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists.  Bartley was a Fellow in our winter cohort who left his tenured faculty position for a job at Sotera. Here’s his story:

 

image01Tell us about your background. How did this set you up to be a great Data Scientist?

I come from an academic background.  My PhD is in Computer Science and Engineering, and I’ve been a full-time college professor since 2006 teaching both undergraduate and graduate students and researching in the areas of logical query optimization and loosely-structured data.

Part of being a great data scientist involves hard skills (e.g., programming and statistics), and yet another part requires excellent soft skills (e.g., effective written and oral communication to all levels of a company).  Having experience with presentations both in the classroom and with college administration allows me to easily speak with employers, and it affords me a level of comfort when I need to distill a highly technical concept for management that may not need the intricate details we scientists love so much.  I was fortunate to have many of these soft skills when starting the fellowship, and networking with the other Fellows, Data Incubator staff, and employers was a great way to practice them (or even acquire them from scratch).

 

Leaving academia must have been hard for you. Why did you want to move into industry, and what were you major challenges?

Being a tenured faculty member and department chair, it might seem odd to want to switch at this point in my career, but I was looking to break out of my then current career trajectory.  I wasn’t afforded the time to work on interesting/meaningful projects and learn all the great new techniques out there, and I honestly felt that my technical skills were starting to age.  I had to quickly come up-to-speed on a wide variety of concepts, both programmatic and statistical, in order to successfully complete the fellowship.

I see that being a challenge for anyone coming from a similar background, but don’t let that deter you.  Applying to and completing the fellowship at The Data Incubator is one of the most rewarding experiences of my career.  I wanted to solve interesting problems, work with bleeding-edge technologies, analyze large datasets, and apply my skills to make a substantial impact.  As a data scientist at my new company, I know that all of these goals are met!

 

What do you think you got out of The Data Incubator?

That’s a tough question to answer.  Being a Fellow with The Data Incubator is one of the highlights of my professional career, and it’s such an all-inclusive experience that it’s difficult to identify single items. One thing that was great about The Data Incubator was the ability to network and form genuine friendships with the other Fellows.  As an academic, I didn’t get an opportunity to interact with colleagues outside of computer science/engineering that much.  With Fellows coming from a wide range of backgrounds (e.g., astrophysics, economics, and neurology), I really had a fantastic opportunity to learn about these other fields and see how what we do in computer science integrates with the larger world.

Another thing I took away from The Data Incubator is the sense of time and how quickly things move in industry.  This is a night-and-day change from higher education where things tend to move extremely slow, and the aggressive timeline for our coursework instilled in me the need to be agile and highly adaptive to current business needs and goals.  I’d be remiss if I didn’t mention how incredibly valuable the face-to-face networking was with the many hiring partners.  It’s an incredible experience to speak one-on-one with so many of these accomplished data scientists and VPs at the trailblazing companies who partner with The Data Incubator.

 

Could you tell us about the mini-projects you worked on? How did they help?

I completed several mini-projects (typically 2-3 each week) in lieu of a fellowship-spanning single project.  The educator in me likes the idea of a culminating capstone-like project that employers can view and gauge my skills, and I honestly thought not having this would be detrimental to me securing a job at the end of the fellowship.  I was 100% wrong.  The mini-projects forced me to both learn completely new skills and learn new ways to apply skills I already had.  For example, I had absolutely no experience with Web scraping before the first week of the fellowship.  One of the mini-projects that week tasked us with doing exactly this in a language (Python) with which I wasn’t the most familiar.

Tackling these multiple challenges on a tight timeframe forced me to learn the new techniques/skills quickly.  It also forced me to adapt my approach quickly when something wasn’t quite working out.  As a data scientist, you must be able to vary your approach and technique when something isn’t working, and these mini-projects were excellent practice for this on real-world datasets.  Since the scope and nature of the mini-projects varied, I found it easy to talk about these experiences and my results during virtually every interview.

 

What advice would you give to someone who is applying for The Data Incubator, particularly anyone with a CS background?

One of the most beneficial things a potential Fellow with The Data Incubator can do is actually take the time to complete the pre-fellowship coursework (the 12-day jumpstart program).  The Data Incubator is incredibly challenging yet also rewording, and this pre-fellowship coursework really helped me be ready to start on day one.  For those coming from a CS/CompE background, I’d highly suggest brushing up on your basic probability and some statistics.  This knowledge will help you both in the interviews for the fellowship and once you get accepted to The Data Incubator.  Refresh yourself not just on algorithms but also analysis of algorithms (being able to identify why one is better than the other in certain circumstances).

Try to change your mindset from focusing on implementation to focusing on design and conceptualization.  For those applying who have been in academia (including higher education) for a while, really focus on algorithms and statistics for your application.  You’ll have time to build-out the skills specific to data science during the fellowship.

 

Thanks Bartley! Any final advice or thoughts about The Incubator?

Being a Fellow with The Data Incubator is one of the most challenging yet rewarding experiences I’ve ever had the privilege to complete.  The team at The Data Incubator will help you in any way they can, but like so many other things, what you get out of something is directly proportional to what you put into it.  Commit to the program and to the work, and you will have incredible support and guidance from The Data Incubator team to help you achieve your goals.  Go to the happy hours!  Reach out to the hiring partners that excite you!  Don’t be intimidated to learn new skills!  A fellowship with The Data Incubator is a huge commitment yet immensely rich and rewarding.

 

Learn more about The Data Incubator here.

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