Data & Applied Scientist II

Microsoft•Redmond, WA

20h•Hybrid

About The Position

We are looking for 2 Data & Applied Scientists II to help teams make better product and business decisions through rigorous experimentation, strong statistical thinking, and practical use of AI in everyday analytical work. In this role, you will design and analyze A/B experiments, translate results into clear decisions, and continuously evolve how experimentation is done. 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
OR Master's Degree 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) or consulting experience
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.

Nice To Haves

Demonstrated experience designing, analyzing, and interpreting A/B experiments end‑to‑end.
Solid understanding of experimental design concepts, including hypotheses, control/treatment comparisons, metrics, and evaluation windows.
Ability to identify and reason about common experimentation challenges such as bias, interference, insufficient power, and metric sensitivity.
Experience communicating experimental results clearly, including uncertainty, limitations, and trade‑offs.
Solid foundation in applied statistics (e.g., hypothesis testing, confidence intervals, variance, and basic causal reasoning)
Ability to work with real‑world data that is noisy, incomplete, or imperfect, and still produce reliable insights
Solid judgment in selecting appropriate metrics and analytical approaches for decision‑making
Experience using AI‑assisted tools to support data analysis, experimentation, or insight generation.
Ability to thoughtfully integrate AI into everyday analytical workflows while maintaining statistical rigor.
Curiosity and openness to experimenting with new AI capabilities to improve speed, quality, or clarity of analysis.
Proficiency in SQL for data extraction and analysis.
Experience with at least one analytical programming language (e.g., Python or R).
Familiarity with experimentation analysis workflows, dashboards, or analytical tooling.
Ability to explain complex analytical concepts and experimental results to non‑technical audiences.
Solid written and verbal communication skills focused on driving decisions, not just reporting results.
Experience working cross‑functionally with product, engineering, or design partners.

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.