Economies of Data Science

What is the problem?

For an ML team, “small” usually means having limited resources, manpower, and budget compared to larger teams. While small teams can be more agile, collaborative and focused, overreliance on one or a few individuals, low work reproducibility affects and no standardisation in code development affects the team’s performance.

Why is this important?

Addressing some of the above issues can make day to day life of Data Scientist lot better, right from initial onboarding to deployments. Through improved collaboration and good coding practices, the quality and throughput of work can be improved overall.

Who is this talk for and how will it impact them?

The key takeaway will be for people running/leading small data science teams and learn about tools and processes that can help improve developer productivity and quality of work.

Speaker Background
Chaitanya is a data scientist with a track record of five years in the field. Currently, he serves as a Data Scientist at Locus and has an impressive portfolio of projects covering forecasting, operations research, and NLP. When he’s not whipping up complex algorithms, he’s probably savoring a delicious biryani or capturing life’s precious moments through his lens.