Today we have Apple’s Senior Data Scientist, Farhan Baluch as we deep dive into Data Analytics.

Farhan has over 10 years of combined academic and industrial experience in machine learning. He likes to combine strategy with tactics and enjoy operating both at the micro and macro levels of data science efforts.

As a neuroscientist, he worked on data obtained from sensors used to record neural activity from humans and insects. As a data scientist in the business world, he worked with data from large automotive, telecommunication and other industries as well as led small teams of data scientists at Opera solutions. In his role at Netflix, he brought data science to the world of entertainment and used quantitative methods to bring structure to the problem of predicting the success of content on Netflix.

Currently, in his role at Apple, he’s working on a variety of projects aimed at personalizing the search ad experience within the App Store.

Quotes:

• When I talk about effective data science, and we're specifically talking about data science, which is the combination of sort of machine learning, math, statistics, and in the business domain knowledge, it's purely doing sort of machine learning and AI for a particular purpose, which is to add value to your business. And so when I talk about effective data science, it's really about seeing in that sense, how do you come up with a process or a data science function that brings that value to your business.
• In terms of effective data science, I think you want each function to be doing, what their domain expertise is, and you want as little sort of cross-functional cooperation. Although not necessarily people leaning over onto the keyboard, right?  And I think that's where the effectiveness comes in.
• I think AV testing gives us that power to sort of scale customer feedback, without having to actually do interviews, though, anecdotal interviews do help.

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Read the full episode summary here: Ep 150

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