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Towards Data Science - 39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus
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39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus

Towards Data Science

06/24/20

37m

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Nick Pogrebnyakov is a Senior Data Scientist at Thomson Reuters, an Associate Professor at Copenhagen Business School, and the founder of Leverness, a marketplace where experienced machine learning developers can find contract work with companies. He’s a busy man, but he agreed to sit down with me for today’s TDS podcast episode, to talk about his day job ar Reuters, as well as the machine learning and data science job landscape.

Previous Episode

One Thursday afternoon in 2015, I got a spontaneous notification on my phone telling me how long it would take to drive to my favourite restaurant under current traffic conditions. This was alarming, not only because it implied that my phone had figured out what my favourite restaurant was without ever asking explicitly, but also because it suggested that my phone knew enough about my eating habits to realize that I liked to go out to dinner on Thursdays specifically.

As our phones, our laptops and our Amazon Echos collect increasing amounts of data about us — and impute even more — data privacy is becoming a greater and greater concern for research as well as government and industry applications. That’s why I wanted to speak to Harvard PhD student and frequent Towards Data Science contributor Matthew Stewart about to get an introduction to some of the key principles behind data privacy. Matthew is a prolific blogger, and his research work at Harvard is focused on applications of machine learning to environmental sciences, a topic we also discuss during this episode.

Next Episode

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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