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Manager, Data Science (Product Analytics)

Wikimedia Foundation
CompanyWikimedia Foundation
CategoryData & Analytics
LocationRemote
RemoteRemote
EmploymentNot stated
LevelManager
SalaryNot stated by the employer
Posted19 Feb 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
MANAGER, DATA SCIENCE (PRODUCT ANALYTICS) 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. The Wikimedia Foundation is looking for a Data Science Manager to lead the Product Analytics team , reporting to the Senior Director, Research and Decision Science . In this role, you will lead a team of data scientists whose insights guide strategies and tactics aimed at achieving our vision: a world in which every single human being can freely share in the sum of all knowledge. Your team partners with product managers, designers, engineers, and colleagues across the Wikimedia movement to produce and share actionable and accessible data and insights to inform decision-making within the Foundation and in Wikimedia communities. As the Data Science Manager for Product Analytics, you will manage a team of data scientists who help product development teams use quantitative data and insights to understand our audiences, inform strategy, guide product decisions, and assess the impact of new features. You will be accountable for the work your team delivers, and you will ensure timely delivery, data accuracy, and consistent reporting by creating shared data processes and procedures. And you will support the design of data products and tools that make critical data more accessible to stakeholders and decision makers. 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 platforms and communities. Candidates must be located within the UTC+1 to UTC-8 time zones, available for critical meetings and synchronous work between 14:00 and 22:00 UTC, and able to travel for offsites and conferences (up to 4 times per year). You are responsible for: Informing strategy, guiding product decisions, and assessing the impact of product development work through your team’s timely delivery of accurate quantitative data and insights – balancing the need to operate in a fast-changing environment with appropriate rigor. Proactively making recommendations on product direction and data strategy, surfacing insights and interpreting results. People management for a team of roughly seven data scientists and nurturing a strongly collaborative and inclusive culture of trust, excellence, and empowerment. Mentoring team members on critical thinking, data-informed storytelling, technical skillset, and stakeholder partnership. Continuous development and improvement of experimentation and measurement best practices, product health metrics , visualizations, and reports for easier interpretation and increased accessibility. Partnering with the Experiment Platform & Data Engineering teams to build and maintain data products that make data and insights accessible. Collaborating with product teams – especially product managers – and colleagues in the Research & Decision Science group to cultivate a practice of data-informed product development and decision making. Skills and Experience: Deep experience with experimental design and statistics, applied to internet-scale audience or user experience data. Experience collaborating with product development teams in a fast-paced environment to test, analyze, and evaluate user-facing features.  Prior experience as a people manager. Demonstrated commitment to equity, inclusion, and diversity. Experience with large-scale data processing & storage tools (we use Hadoop, Hive, Presto, and Spark). Qualities that are important to us: Ability to identify where data can have the most impact and clearly communicate findings and recommendations to partner teams Discretion and competence in handling sensitive or confidential data.
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