Karan Sindwani capitalized on the many opportunities offered to M.S. in data science students at Columbia University. The 2020 alumnus served as secretary of the student council, completed a data science internship at Quantiphi, and worked as a research assistant at Columbia University Irving Medical Center. With such well-rounded experience, it’s not surprising that he landed a top job. Today, he is a data scientist at Amazon Web Services. 

Congratulations on your role with Amazon! Can you tell us a bit about your day-to-day?

Most of the work is on new AWS services, which is confidential. But recently I got a chance to explore survival analysis with neural networks. Survival analysis, being a statistical technique, isn’t a powerful technique, but when combined with neural networks and applied in the medical field, it gave state-of-the-art results.

What helped to pique your interest in data science?

A professor at Vellore Institute of Technology, my undergraduate university, who happens to be a Columbia alum himself, challenged me to think beyond the surface-level problems in operations management and research. He encouraged me to think of problems from more of a statistical perspective. As I began to explore more and more modern stats methods, I became interested in the intersection of stats and computer science, and decided to apply to master’s programs in data science. Being from a computer science background really helped me. Algorithms and statistics are essential for your success in data science.

Did you work before starting graduate school?

I worked for two years as a junior data scientist at a startup in New Delhi before starting grad school. I focused on building recommender systems and chatbots.

Why did you choose Columbia?

Columbia was my dream school. It is one of few programs that offers data science, as opposed to a computer science degree with a specialization. The curriculum has core courses in statistics, algorithms, and machine learning, which build a great foundation to deep dive in deep learning areas. It was my first time living in New York City and the experience was nothing short of amazing. Being from New Delhi, the capital of India, I was used to a fast-paced life, but NYC offers way more than that. The world’s most cosmopolitan city casts its charm over everyone. The city is always awake, from late night parties to early morning hikes…I got the chance to do everything. 

What was your favorite data science course?

Applied Deep Learning with Josh Gordon. The course pushed us to pursue novel projects in data science. It was always focused on the application of our work in the real world, and Gordon kept us updated with the latest papers in data science.

Tell us about your internship experience.

I did my internship in Quantiphi, a data science company in the Boston area. The internship helped me understand the lifecycle of data science modeling. How one would monitor it, learn incrementally, and make the field of machine learning easier for the stakeholders to understand.

What was your capstone project?

My capstone project was with Capital One. The project was to build an unsupervised entity resolution system using a graphical approach. We were provided with a base paper, but given the freedom to explore options. It was a really challenging use case, as it was different from the classical ML/DL problems. We had to deep dive into graph-based approaches, and we built our model from scratch.

— Sharnice Ottley