41. Solmaz Shahalizadeh - Data science in high-growth companies
Towards Data Science
07/08/20
•45m
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One of the themes that I’ve seen come up increasingly in the past few months is the critical importance of product thinking in data science. As new and aspiring data scientists deepen their technical skill sets and invest countless hours doing practice problems on leetcode, product thinking has emerged as a pretty serious blind spot for many applicants. That blind spot has become increasingly critical as new tools have emerged that abstract away a lot of what used to be the day-to-day gruntwork of data science, allowing data scientists more time to develop subject matter expertise and focus on the business value side of the product equation.
If there’s one company that’s made a name for itself for leading the way on product-centric thinking in data science, it’s Shopify. And if there’s one person at Shopify who’s spent the most time thinking about product-centered data science, it’s Shopify’s Head of Data Science and Engineering, Solmaz Shahalizadeh. Solmaz has had an impressive career arc, which included joining Shopify in its pre-IPO days, back in 2013, and seeing the Shopify data science team grow from a handful of people to a pivotal organization-wide effort that tens of thousands of merchants rely on to earn a living today.
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40. David Meza - Data science at NASA
July 1, 2020
•40m
Machine learning isn’t rocket science, unless you’re doing it at NASA. And if you happen to be doing data science at NASA, you have something in common with David Meza, my guest for today’s episode of the podcast.
David has spent his NASA career focused on optimizing the flow of information through NASA’s many databases, and ensuring that that data is harnessed with machine learning and analytics. His current focus is on people analytics, which involves tracking the skills and competencies of employees across NASA, to detect people who have abilities that could be used in new or unexpected ways to meet needs that the organization has or might develop.
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Machine learning in grad school and machine learning in industry are very different beasts. In industry, deployment and data collection become key, and the only thing that matters is whether you can deliver a product that real customers want, fast enough to meet internal deadlines. In grad school, there’s a different kind of pressure, focused on algorithm development and novelty. It’s often difficult to know which path you might be best suited for, but that’s why it can be so useful to speak with people who’ve done both — and bonus points if their academic research experience comes from one of the top universities in the world.
For today’s episode of the Towards Data Science podcast, I sat down with Will Grathwohl, a PhD student at the University of Toronto, student researcher at Google AI, and alum of MIT and OpenAI. Will has seen cutting edge machine learning research in industry and academic settings, and has some great insights to share about the differences between the two environments. He’s also recently published an article on the fascinating topic of energy models in which he and his co-authors propose a unique way of thinking about generative models that achieves state-of-the-art performance in computer vision tasks.
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