Senior Data & Applied Scientist

Microsoft • Redmond, WA

1d • $119,800 - $261,000 • Hybrid

About The Position

We are looking for a Senior Data & Applied Scientist to help teams make better product and business decisions through rigorous experimentation, solid statistical thinking, and practical use of AI in everyday analytical work. This is a hands‑on role for someone who enjoys learning, questioning assumptions, and applying data science to real‑world decisions at scale. This is not a “reporting” role. It is a decision‑making role, where experimentation, judgment, and AI‑enabled workflows come together to shape real outcomes. 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. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Requirements

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• equivalent experience.

Nice To Haves

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• equivalent experience.

Responsibilities

• Design, analyze, and interpret A/B experiments end‑to‑end, from hypothesis formulation to final decision
• Choose appropriate metrics, success criteria, and evaluation windows based on user behavior and business context.
• Identify and diagnose common experimentation issues (e.g., bias, interference, power limitations, metric sensitivity).
• Communicate experimental results clearly, including uncertainty, limitations, and trade‑offs.
• Go beyond “did it move the metric?” to explain why results happened and what decision should be made
• Combine experimental evidence with observational analysis when appropriate
• Partner closely with product, engineering, and design stakeholders to influence direction using data
• Use AI tools to accelerate analysis, exploration, and insight generation (e.g., faster hypothesis testing, code generation, narrative summaries).
• Continuously evaluate where AI can improve experimentation workflows, without compromising rigor or correctness.
• Develop good judgment about when to rely on automation vs. when deep statistical reasoning is required.
• Stay current on experimentation methods, causal inference, and applied statistics.
• Learn and adopt new tools, techniques, and best practices quickly.
• Contribute to shared standards and documentation that improve how teams run experiments and make decisions.

Benefits

• Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed