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.
Tell us about your background. How did it set you up to be a great Data Scientist?
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).
What do you think you got out of The Data Incubator?
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.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with a CS background?
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.
Leaving academia must have been hard for you. Why did you want to move into industry, and what were you major challenges?
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!
Could you tell us about the mini-projects you worked on? How did they help?
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.