Wikimedia foundation

Overview

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.

About Wikimedia foundation

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

Requirements & Eligibility

• Experience with experimental design and statistics or machine learning methods
• Experience using AI coding assistants for data analysis and other technical tasks
• Fluency in R or Python, and common version control and command-line-based tools, for data analysis, statistical modeling, simulation, visualization, & reporting
• Fluency in SQL and working with large scale data (we use tools like Hive, Presto, Druid, and Spark)
• Experience collaborating with product development teams in a fast-paced environment to test, analyze, and evaluate user-facing features for an online platform

Key Responsibilities

• 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
• 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