Data are becoming the new raw material of business
The Economist

Engineering a New Career in Data Science: Alumni Spotlight on Abhishek Mishra

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. Abhishek was a Fellow in our Winter 2015 cohort in Washington, DC who landed a job as a Data Scientist at Samsung SDS.


Tell us about your background. How did it set you up to be a great Data Scientist?

I have a PhD in electrical and computer engineering from Lehigh University. I already had a good background in probability, statistics, and mathematical optimization that helped me in understanding the essence of data science. I was part of the Electrical and Computer Engineering Department and I was working on making the Internet more secure, by developing theoretical frameworks for timing attacks on anonymous networks such as TOR.

One of the key contributions of my PhD work was that I was able to find the closed form characterization of maximum achievable anonymity in a simple Chaum Mix (the basic building blocks of an anonymous network).

One important thing that I learnt from my PhD work was how to do research. This process consists of following four parts:

  1. Find out an interesting problem to work on.
  2. Formulate the problem in a concrete mathematical framework.
  3. Find out the mathematical tools required to solve the problem.
  4. Convince your adviser and the world that you solved an important problem worth publishing in a reputed conference or journal.

This whole process allowed me to work on an unstructured problem. I had to keep my eyes open to find the problem in the domain I was working on. This whole process helped me to become a better data scientist by just changing the role a little bit. Now I keep an open eye for finding any interesting patterns in the data. Anything that is unexpected is interesting.


What do you think you got out of The Data Incubator?

There are tons of things that I got from The Data Incubator. First, The Data Incubator introduced me to a nice and comprehensive overview of the current techniques in data science. That includes everything from linear regression to Spark. Second, by hearing from many different companies, I got a feeling for the different types of problems that they tackle and how data science offers real solutions in a variety of fields. The Data Incubator exposed me to how data science is actually used by companies. Finally, The Data Incubator helped expand my network both by introducing me to companies looking for data scientists, and by introducing me to the other Fellows.


What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?

My first advice is to learn Python and machine learning. My second advice is to solve a Kaggle Challenge. I would suggest not only solve it, but showcase your result, using visualizations, to a lay person, who does not know any machine learning, and convince him or her. By doing this, you will learn two important points about data science: analysis and convincing others. From my experience, I find the second part harder.


What is your favorite thing you learned at The Data Incubator?

My favorite thing about The Data Incubator was my awesome peer group. My peer group had so many smart and dedicated students and they constantly inspired me to put my best in the work. I really enjoyed my cohort, the new tools I learnt, and the morning lectures. I enjoyed discussing with fellows and learning through interactions. I also enjoyed my daily morning travel from Baltimore to DC.


Could you tell us about your Data Incubator Capstone project?

I worked on sentiment analysis for a Yelp Data Set.


And lastly, tell us about your new job!

I am working with Samsung SDSRA group in San Jose, CA. I am working on deep learning.


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