Today we're joined by Parvez Ahammad, head of data science applied research at LinkedIn.
In our conversation, Parvez shares his interesting take on his organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long term projects are managed, how to identify products that can help in a cross-cutting way across multiple lines of business, quantitative methodologies to identify unintended consequences in experimentation, and navigating the tension between research and applied ML teams in an organization. Finally, we discuss differential privacy, and their recently released GreyKite library, an open source Python library developed to support forecasting.
