Podcasts Technology Datacast Episode 35: Data Science For Food Discovery with Ankit Jain

Episode 35: Data Science For Food Discovery with Ankit Jain

Published: June 20, 2020, 8 p.m.

Show Notes

  • (2:27) Ankit studied Electrical Engineering with a focus on Communication and Signal Processing at the Indian Institute of Technology, Bombay.
  • (3:27) Ankit then worked for three years as a Senior Field Engineer at Schlumberger, an international oilfield services company.
  • (4:23) Ankit then went to the US to pursue a Masters in Financial Engineering from the Walter Hass School of Business at UC Berkeley.
  • (6:13) Ankit had an opportunity to intern as a data scientist at Facebook during his Masters and worked on detecting spam for Facebook pages.
  • (8:27) Ankit worked full-time as a Quantitative Finance Analyst at Bank of America after finishing his degree, with projects such as building models to identify risk in bank portfolio and analyzing relevance opportunities for strategic investment.
  • (9:46) Ankit discussed his transition to a Data Scientist role at ClearSlide, a B2B platform for Sales Enablement + Engagement.
  • (11:32) Ankit discussed his work on sales forecasting algorithms at ClearSlide.
  • (15:06) In 2015, Ankit moved to Bangalore to become the Head of Data Science and Analytics at Ruunr, a B2B platform that offers hyper-local logistics services that partners with merchants in India.
  • (16:18) Ankit unpacked his thorough post “How Food Delivery Can Be a Sustainable Business” that reflects his experience at Ruunr.
  • (18:35) Ankit talked about the similarities and differences of tech culture in Bangalore and San Francisco.
  • (19:39) Ankit came back to the US and started working as a Data Scientist at Uber in early 2017.
  • (20:34) Ankit discussed his work at Uber on user-level forecasting.
  • (23:12) Ankit talked about the different types of problems that researchers at Uber AI Labs work on.
  • (24:49) Ankit unpacked his in-depth technical post on Uber’s Engineering blog “Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations” — including graph neural networks for food recommendations, the design of the data and training pipeline, and ways to incorporate more data for further improvement.
  • (28:55) Ankit discussed the challenges with building the Uber Eats recommendation system in production.
  • (32:15) Ankit has written a technical book called TensorFlow Machine Learning Projects — which teaches how to exploit the benefits (simplicity, efficiency, and flexibility) of using TensorFlow in various real-world projects.
  • (34:43) Ankit gave his two cents on the battle of frameworks between TensorFlow and PyTorch.
  • (36:26) Ankit shared his advice for academics looking to work in the industry: building end-to-end projects, learning how to build scalable pipelines, and keeping up with important research topics.
  • (38:33) Ankit reflected on the benefits of his electrical engineering and financial analysis education towards his career in data science.
  • (40:11) Closing segment.

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