At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. am Swift was a Fellow in our first cohort who landed a job heading up data science at Betterment, one of our hiring partners. He had previously received his PhD in Industrial Administration at Carnegie Mellon.
Sam, tell our audience about your background:
I was part of the first undergraduate class in Decision Science, which combined the psychological and economic perspectives on human behavior. I went back to CMU to complete a PhD in Organizational Behavior at the Tepper School of Business, where I conducted lab and field research on decision-making and negotiation.
Along the way, I’ve developed expertise in software development and statistical programming with R. I spent three years as a developer in a software consulting startup and two years as a postdoc managing a team of statisticians and developers. The fact that I was passionate about behavioral economics and decision-making, but also really enjoy working in a software development environment, contributed to my decision to ultimately pursue new opportunities outside of academia.
Currently, I am a data scientist at Betterment, the largest and fastest-growing* automated investing service, where I am responsible for insights into and analyses of our customer’s investing behavior.
What do you think you got out of The Data Incubator?
I also realized that there are many data scientist positions for which I am not a great fit, but that matching is a two-way street. Rejection feels like a big setback but is likely an indication that the job would not have been fun and fulfilling. The interviews that went poorly were actually helpful and informative because they were likely early signals that I would not have enjoyed the position going forward. In my case, the interview with Betterment stood out as a great fit on both culture and skill requirements, and they agreed.
The intense incubator experience was also a great way to quickly transition my thinking and language from academic abstraction to business pragmatism. Like miscommunication between any two fields, I found that there was lots of common ground on ideas, but that it was obfuscated by specialized jargon on both sides.
There is also a learning process around the tradeoff between creating something useful and creating something perfect. The business environment demands more emphasis on the former, which is a lesson I’ve learned more than once. The Data Incubator gave me a great head start on those transitions, allowing me to focus on more important substantive ideas during interviews and my first weeks on the job.
