New 
Senior Data Scientist
 Microsoft | |
remote work
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 United States, Texas, Irving | |
| 7000 State Highway 161 (Show on map) | |
 Jul 31, 2025 | |
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OverviewDo you want to shape how Windows is built and shipped? Join the team behind the experimentation engine that powers product innovation and deployment decisions. At Windows Experimentation, we help teams move fast and ship smart by delivering trusted insights through randomized controlled trials.As a Senior Data Scientist on the Windows Experimentation team, you'll build the statistical and technical foundations that power experimentation across the Windows ecosystem. Your work will enable product teams to test new features with confidence and help engineering and release teams make data-driven decisions about shipping Windows. You'll collaborate across disciplines to design scalable systems, improve causal inference methods, and ensure that experimentation is fast, reliable, and trustworthy.This role offers high-impact work at the intersection of science, engineering, and product; a deep focus on experimentation infrastructure, causal inference, and telemetry at scale; and the opportunity to shape how Microsoft builds and ships Windows to more than a billion devices.This position is flexible to remote work within the United States. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
ResponsibilitiesDevelop and apply statistical methods to support randomized controlled trials (RCTs) that inform both product development and deployment decisions across the Windows ecosystem.Design scalable systems and tooling that enable rigorous, high-throughput experimentation across the Windows ecosystem.Partner with engineering, product, and data science teams to define metrics, validate experimental designs, and ensure robust causal inference.Investigate experiment outcomes and anomalies to surface insights, improve decision quality, and strengthen system observability.Advance experimentation best practices by contributing to internal guidance, tooling, and methodology.Stay current with advances in causal inference, experimentation platforms, and statistical methodology and bring fresh thinking from academia and industry into our work. | |