Senior Data Scientist, Safety & Security

Summary

Wikipedia is a trusted source of knowledge the world over, read by over a billion people a month in over 300 languages. It’s offered for free and operated independently by the non-profit Wikimedia Foundation (that’s us), powered mainly by small donations.

We are hiring a Senior Data Scientist to help guide Wikipedia’s anti-abuse and security strategy as a member of our Product Analytics team. In this role, you will inform our product development strategy by collaborating with product managers, engineers, and others to help our features and interventions make the impact they should.

As the Senior Data Scientist, Safety & Security, you will partner closely with the Product Safety and Integrity team, whose strategy is built around bringing cutting-edge AI and machine learning techniques to bear on difficult problems in the field of safety and security. You will amplify the impact of the safety and security roadmap through creative data analysis, identifying the right metrics for the job, and developing data strategies that advance our product decisions.

This role is an opportunity to be at the center of a pivotal time for Wikipedia, as AI changes how people access knowledge and presents new risks and opportunities in online safety and security.

You are responsible for

• Serving as a strategic partner for product managers, engineers, designers, and other colleagues in product development teams.
• Proactively making recommendations on product direction and data strategy, surfacing insights and interpreting results.
• Timely delivery of accurate quantitative data and insights to inform strategy, guide product decisions, and assess the impact of product development work – balancing the need to operate in a fast-changing environment with appropriate rigor.
• Clearly communicating results and data-backed recommendations to stakeholders across different functions, ensuring understanding and guiding decision-making.
• Building and executing measurement strategy, evaluating experiments using Bayesian & Frequentist approaches, and performing quantitative research to inform product decisions.
• Developing queries & code to work with large-scale internal and external data repositories, using cutting-edge AI assistants, and tools such as Apache Iceberg, Hive, Druid, Presto, and Spark.
• Building automated, self-service dashboards for product teams that track success and health metrics using tools such as DBT, Airflow, and Superset.
• Helping product team members instrument features, perform data quality assurance, and use tools for running A/B tests.
• Demonstrating good judgment in prioritizing workload and selecting analytical methods and techniques.