At The Data Incubator we run a free eight-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. Sumanth was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Revon.
Tell us about your background. How did it set you up to be a great data scientist?
I did my bachelors degree in Chemical Engineering at the University of Delaware and my PhD in Applied Mathematics at Northwestern University. After some postdoctoral work between Northwestern and Oxford University, I went into industry as a quantitative consultant for W.L. Gore & Associates. For the past 4 years, I have spent most of my time delivering technology solutions at W.L. Gore, teaching mathematics at the University of Delaware, and performing and teaching Indian Classical Music.
On the question of what makes a strong data scientist, I think that the better practitioners in the field tend to be hypothesis driven, strong critical thinkers with hard skills in statistics, programming, mathematics, and hardware. Hence, my background in engineering and mathematics, my consulting experience, and my years of teaching probably contributed the most to my success.
What do you think you got out of The Data Incubator?
1. I learned an incredible amount of new problem solving methods, concepts and technologies
2. I joined a large community of practicing and aspiring data scientists (the fellows admitted into this program were really accomplished and came from all educational backgrounds)
3. I was approached and interviewed by numerous companies of all sizes.
4. I received professional advice from hiring managers, computer scientists, and strong mathematical talent.
Could you tell us about your Data Incubator project?
The goal of my project was to predict the probability that a technology patent would go through litigation using data that exists in freely available patent XMLs. The data sets were scraped mostly from google bulk patent.
I compared the success rates of a variety of machine learning classifiers in correctly identifying litigated and unlitigated patents. The features used in the classifiers included both intrinsic patent literature characteristics and post patent filing events. It was an interesting project. I didn’t expect the algorithm to be as successful as it was!
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
Definitely do your homework! Folks in applied mathematics generally know something about physics, numerical methods, and a broad range of mathematical concepts. These are all good things! Applied mathematicians might not, however, be algorithms experts. They may also be novices in statistics and data handling tools.
1) If you want to get through the incubator challenge test and interviews, it would be useful to brush up on efficient algorithms and writing clean code (project Euler is really helpful). You’ll also want to learn how to manipulate and query tables (sql or R)
2) Sharpening your professional skill set is really helpful for the program as well as for job interviews. Good writing and communication skills, strong critical thinking, an ability to work with different people in small and large teams, an understanding of deadlines and associated responsibilities, etc are all useful and marketable qualities.
What’s your favorite thing you learned while at The Data Incubator? This can be a technology, concept, or whatever you want!
The sections on distributed computing (Hadoop, Map Reduce/Spark) were really interesting and useful. They were subjects that I had barely touched in the past. In general, the programming challenges in the mornings and the lectures were fun. Probably the best part was being around the most talented people.
Where are you going to be working? And tell us a little about your new job!