30. Interviewing the Medium data science team
Towards Data Science
04/22/20
•43m
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Revenues drop unexpectedly, and management pulls aside the data science team into a room. The team is given its marching orders: “your job,” they’re told, “is to find out what the hell is going on with our purchase orders.”
That’s a very open-ended question, of course, because revenues and signups could drop for any number of reasons. Prices may have increased. A new user interface might be confusing potential customers. Seasonality effects might have to be considered. The source of the problem could be, well, anything.
That’s often the position data scientists find themselves in: rather than having a clear A/B test to analyze, they frequently are in the business of combing through user funnels to ensure that each stage is working as expected.
It takes a very detail-oriented and business-savvy team to pull off an investigation with that broad a scope, but that’s exactly what Medium has: a group of product-minded data scientists dedicated to investigating anomalies and identifying growth opportunities hidden in heaps of user data. They were kind enough to chat with me and talk about how Medium does data science for this episode of the Towards Data Science podcast.
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29. Cameron Davidson-Pillon - Data science at Shopify
April 15, 2020
•40m
If you want to know where data science is heading, it helps to know where it’s been. Very few people have that kind of historical perspective, and even fewer combine it with an understanding of cutting-edge tooling that hints at the direction the field might be taking in the future.
Luckily for us, one of them is Cameron Davidson-Pillon, the former Director of Data Science at Shopify. Cameron has been knee-deep in data science and estimation theory since 2012, when the space was still coming into its own. He’s got a great high-level perspective not only on technical issues but also on hiring and team-building, and he was kind enough to join us for today’s episode of the Towards Data Science podcast.
Next Episode
31. Russell Pollari - Building habits and breaking into data science
April 29, 2020
•41m
Most of us want to change our identities. And we usually have an idealized version of ourselves that we aspire to become — one who’s fitter, smarter, healthier, more famous, wealthier, more centered, or whatever.
But you can’t change your identity in a fundamental way without also changing what you do in your day-to-day life. You don’t get fitter without working out regularly. You don’t get smarter without studying regularly.
To change yourself, you must first change your habits. But how do you do that?
Recently, books like Atomic Habits and Deep Work have focused on answering that question in general terms, and they’re definitely worth reading. But habit formation in the context of data science, analytics, machine learning, and startups comes with a unique set of challenges, and deserves attention in its own right. And that’s why I wanted to sit down with today’s guest, Russell Pollari.
Russell may now be the CTO of the world’s largest marketplace for income share mentorships (and the very same company I work at every day!) but he was once — and not too long ago — a physics PhD student with next to no coding ability and a classic case of the grad school blues. To get to where he is today, he’s had to learn a lot, and in his quest to optimize that process, he’s focused a lot of his attention on habit formation and self-improvement in the context of tech, data science and startups.
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