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. Isaac was a Fellow in our Fall 2014 cohort who landed a job with one of our hiring partners, Quantitative Brokers, after receiving his PhD in Physics from the University of Pennsylvania.
Isaac, you have a pretty diverse academic background. Can you tell us a bit about that?
What do you think you got out of The Data Incubator?
I think that the most valuable thing that I got from the Data Incubator was a real immersion in industry culture. It was immensely helpful to talk openly with the different employers, to build a sense of what different companies value, and a sense of how to adjust my own diction and mannerisms to signal an appreciation of what they value. I feel that just being in frequent contact with the Data Incubator’s Hiring Partners really helped me know how to present myself when interviewing, and additionally helped me build a much better sense of what sort of company I wanted to work for.
Almost as valuable as the cultural immersion were the mock-interview sessions. Tech interviews remind me of the SAT: you can greatly improve your score by building a familiarity with what sort of questions tend to show up. Having frequent practice in answering technical questions, time-pressure and all, really helped keep me in the right mindset to perform well in interviews.
What elements of your Computational Neuroscience training do you think have been useful for your current work as a data scientist?
Can you tell us about your project at the Incubator?
In the end about half the work on my project went towards getting all the data into a usable format, and the other half into building the model itself, validating the model, and doing control analyses to make sure that the model was actually as useful as it seemed to be. By the end of the program I had a fairly convincing product that could pick out good loans at a rate well above chance, and I had a set of analyses that gave a good estimate of just how much money the product was worth. [Isaac was too shy to admit this, but his returns were 15% higher than those in the dataset.]
Thanks for your time, Isaac! In closing, what advice would you give to someone who is applying for The Data Incubator?
If you’re still in the process of applying, I would say to make sure you review your computer-science, probability theory, and math. You’ll be competing against a lot of quite talented people with strong quantitative backgrounds, and having a good grounding in quantitative problem-solving will be important for passing the interviews.
Once you’re in, I don’t know that you’ll need much in the way of advice. It’s a good program, and if you show up, stay engaged, and focus on your own weak-points then you’ll get a lot out of it. The one thing I will say is that you should be prepared to take on a mindset different from what you may be used to in academia. This may not be true for all people, but in academia it’s easy to settle into the mindset of “what problems sound interesting?” (interesting either to you, or to your advisor’s grant committee) whereas in industry it seems that the much more important position is often “what will be useful?” You may be asked to take a much more goal-oriented approach than you are used to, and you may have to put more thought into specific problems that you are trying to solve, or practical metrics that you are trying to improve.
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