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Reflections On Data Science In Big Tech | Varun's blog

varunprajan.github.io · 3,209 words · saved by 1 readers

Data science is an infamously nebulous term. It can, depending on the organization, involve building dashboards, writing data pipelines, developing metrics, conducting causal inference analyses, doing ad-hoc analytics work, pulling numbers for stakeholders who don’t know SQL, building ML models offline, deploying those ML models to production, writing data tests and monitoring data “drift”, running A/B tests, or simply being a more quantitatively-minded PM. At “big tech” companies, however, the term is somewhat less vague — particularly if we focus our attention on “product data scientists” (as opposed to, say, marketing, ads, or other activities). In my brief (2 year) stint at Spotify, I was a product data scientist. I supported an engineering team that made new playlists and better versions of existing ones. More generally, here is my definition of what product data scientists do: they use quantitative methods (analytics, statistics, and, occasionally, machine learning) to help their

Data science is an infamously nebulous term. It can, depending on the organization, involve building dashboards, writing data pipelines, developing metrics, conducting causal inference analyses, doing ad-hoc analytics work, pulling numbers for stakeholders who don’t know SQL, building ML models offline, deploying those ML models to production, writing data tests and monitoring data “drift”, running A/B tests, or simply being a more quantitatively-minded PM. At “big tech” companies, however, the term is somewhat less vague — particularly if we focus our attention on “product data scientists” (a

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