We are looking for Data Scientist with strong ML thinking to set standards for how Facebook measures whether Artificial Intelligence is implemented responsibly. As part of Facebook's central Responsible AI (RAI) team, our job is to ensure that teams across the company are building AI-powered products which are fair and robust, enable transparency and control, and meet expectations for privacy, security, and accountability. RAI Analytics supports this by developing the methodologies, best practices, and data-sets required for measuring responsibility and by collaborating with product teams to help them set and meet their goals. This role will take a lead in creating an analytics of Robustness and Transparency. It will focus on developing methodologies to validate the guarantees that ML models make, explain how they are intended to behave, and ensure that these expectations are met. It will then work on scaling these measurements company-wide, across our billions of users.
Data Scientist, Analytics - Responsible AI Responsibilities
Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products.
Define problems and opportunities in a complex or ambiguous area.
Inform, influence, support, and execute our product decisions and product launches.
Partner with Product and Engineering teams to solve problems and identify new levers to improve user experience.
Monitoring key product metrics, understanding root causes of changes in metrics.
Influencing product teams through presentation of data-based recommendations, and clearly communicating state of business, experiment results, etc. to product teams.
Advanced degree in Computer Science, Engineering, Math/Statistics, Physics, Economics, or equivalent practical experience.
5+ years experience solving analytical problems using quantitative approaches.
Experience in SQL or other programming languages.
Understanding of statistics (eg, hypothesis testing, regressions, ML systems).
Experience communicating the results of analyses with product and leadership teams to influence the strategy of the product.
Experience in machine learning, deep learning, statistical analysis, or social network analysis.
Development experience in any Scripting language (Python, R, etc.).
Experience with distributed computing (Hive/Hadoop).
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