6. Jay Feng - Data science in the startup world
Towards Data Science
09/25/19
•45m
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I’ve said it before and I’ll say it again: “data science” is an ambiguous job title. People use the term to refer to data science, data engineering, machine learning engineering and analytics roles, and that’s bad enough. But worse still, being a “data scientist” means completely different things depending on the scale and stage of the company you’re working at. A data scientist at a small startup might have almost nothing in common with a data scientist at a massive enterprise company, for example.
So today, we decided to talk to someone who’s seen data science at both scales. Jay Feng started his career working in analytics and data science at Jobr, which was acquired by Monster.com (which was itself acquired by an even bigger company). Among many other things, his story sheds light on a question that you might not have thought about before: what happens to data scientists when their company gets acquired?
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5. Rocio Ng - Data science and product management at LinkedIn
September 19, 2019
•45m
Most software development roles are pretty straightforward: someone tells you what to build (usually a product manager), and you build it. What’s interesting about data science is that although it’s a software role, it doesn’t quite follow this rule.
That’s because data scientists are often the only people who can understand the practical business consequences of their work. There’s only one person on the team who can answer questions like, “What does the variance in our cluster analysis tell us about user preferences?” and “ What are the business consequences of our model’s ROC score?”, and that person is the data scientist. In that sense, data scientists have a very important responsibility not to leave any insights on the table, and to bring business instincts to bare even when they’re dealing with deeply technical problems.
For today’s episode, we spoke with Rocio Ng, a data scientist at LinkedIn, about the need for strong partnerships between data scientists and product managers, and the day-to-day dynamic between those roles at LinkedIn. Along the way, we also talked about one of the most common mistakes that early career data scientists make: focusing too much on that first role.
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For today’s podcast, we spoke with someone who is laser-focused on considering this second possibility: the idea that data science is becoming an engineer’s game. Serkan Piantino served as the Director of Engineering for Facebook AI Research, and now runs machine learning infrastructure startup Spell. Their goal is to make dev tools for data scientists that make it as easy to train models on the cloud as it is to train them locally. That experience, combined with his time at Facebook, have given him a unique perspective on the engineering best practices that data scientists should use, and the future of the field as a whole.
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