31. Russell Pollari - Building habits and breaking into data science
Towards Data Science
04/29/20
•41m
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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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30. Interviewing the Medium data science team
April 22, 2020
•43m
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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If I were to ask you to explain why you’re reading this blog post, you could answer in many different ways.
For example, you could tell me “it’s because I felt like it”, or “because my neurons fired in a specific way that led me to click on the link that was advertised to me”. Or you might go even deeper and relate your answer to the fundamental laws of quantum physics.
The point is, explanations need to be targeted to a certain level of abstraction in order to be effective.
That’s true in life, but it’s also true in machine learning, where explainable AI is getting more and more attention as a way to ensure that models are working properly, in a way that makes sense to us. Understanding explainability and how to leverage it is becoming increasingly important, and that’s why I wanted to speak with Bahador Khaleghi, a data scientist at H20.ai whose technical focus is on explainability and interpretability in machine learning.
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