Raaid Ahmad, Head of Data Science & Engineering for Blockchain @ Facebook, wants to answer your questions about data science! Between now and 22 February, reply to this post with your questions for Raaid and he will respond by 1 March.
What questions do you have about how to get started in a data science career? Are you wondering what skills you should learn? How to interview for a job in data science? How can data scientists drive product leadership? What is the best way to build a data science team? Or maybe you want suggestions for ensuring that a data science team is adding value within a company.
Raaid has broad data science and analytics experience and welcomes your questions about data science as a function and career path. Note that he won’t be able to answer questions about his current work.
About the Q&A Host
Raaid Ahmad has been doing Internet things for 20+ years and investing for 15+. His experience spans the consumer internet, investment & finance (private and public), and gaming industries. He also advises and invests in startups that demonstrate commitment to data-informed decision-making.
He currently works at Facebook leading Data Science & Engineering for the Blockchain team. He also serves on the investment committee of 4DX Ventures, an early stage venture firm focused on Africa. He also advises startups OpenInvest and Domino Data Lab.
Prior to Facebook, Raaid was VP of Analytics and Data Science at Weebly, where he started as the first analyst and scaled the team to about two dozen members including business analysts, data scientists, and data engineers in less than 2 years. In past lives he served as the Director and Head of Analytics for mobile gaming company Kiwi and spent half a decade at Bridgewater Associates, the world's largest hedge fund, where he eventually directed strategy and execution of the firm's high-risk trades. He has additional work experience in world-class competitive poker, economic research, teaching and personal finance publishing.
His passions are (1) Fusing qualitative and quantitative data with rigorous logic to de-risk decisions, develop proprietary product value, and better predict winning long-term strategies, (2) Scaling teams to efficiently solve enormous problems, and (3) Improving financial access and literacy for everyone in the world. His business units have been responsible for the entire data lifecycle: collection, storage architecture, insights, algorithms, and business operations. They have built it in-house, utilized third parties, and implemented hybrid solutions.
His areas of specialty are predictive modeling, network effects, behavioral targeting, risk analytics, adversarial games, trading strategies, poker, fantasy sports, experimental design, and personal financial planning.