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. 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?
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?
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.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
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 is your favorite thing you learned at The Data Incubator?
Could you tell us about your Data Incubator Capstone project?
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!
And lastly, tell us about your new job!
The interesting differentiation in our app is that it includes a machine learning algorithm for triaging patients (predicting whether a patient is ok, should call the doctor, or should go to the ER based on the data that the patient enters). The app is currently triaging patients with COPD (chronic obstructive pulmonary disease) comparably or more accurately than physicians.
I am the lead in developing algorithms for a variety of chronic illnesses. As the company is a startup, I am also doing a variety of physician and patient interviews, research, and generation of new business cases.
Visit our website to learn more about our offerings:
- Data Science Fellowship – a free, full-time, eight-week bootcamp program for PhD and master’s graduates looking to get hired as professional Data Scientists in New York City, Washington DC, San Francisco, and Boston.
- Hiring Data Scientists
- Corporate data science training
- Online data science courses: introductory part-time bootcamps – taught by our expert Data Scientists in residence, and based on our Fellowship curriculum – for busy professionals to boost their data science skills in their spare time.