Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn.
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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.
Thanks to our Sponsor!
Thank you to our friends at LinkedIn for their continued support and for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Connect with Parvez!
- Feb ‘21 – Building, Adopting, and Maturing LinkedIn’s ML Platform with Ya Xu – #453
- Feb ‘20 – Practical Differential Privacy at LinkedIn with Ryan Rogers – #346
- Greykite: A flexible, intuitive, and fast forecasting library