Join Microsoft in Redmond, WA (onsite) to lead end-to-end yield optimization for Azure infrastructure. In this role, you’ll translate ambiguous business problems into actionable insights by combining analytics, machine learning, causal inference, and visualization. You’ll help shape pricing and resource allocation strategies while measuring business outcomes and aligning stakeholders around clear success measures.
Compensation: USD 119,800 - 261,000 per year base pay. The typical U.S. base pay range is USD 119,800 - 234,700 per year. Location-specific ranges apply for the San Francisco Bay area and New York City metropolitan area (USD 160,200 - 261,000 per year).
What you’ll work on
- Write efficient, readable code in Python, SQL, KQL (or similar) to prepare and analyze large-scale revenue, hardware, and capacity datasets using distributed data-processing systems.
- Identify pricing and yield opportunities by selecting, developing, and validating statistical and machine-learning approaches for resource allocation and pricing.
- Assess methodological limitations, along with both statistical and business significance, to ensure recommendations are grounded and decision-ready.
- Partner with business planning, engineering, product management, and finance to define objectives, prioritize analytical work, and align yield strategies with business goals.
- Influence key decisions with evidence, clearly communicate trade-offs, and drive alignment on success measures and implementation.
- Own experiment design and impact measurement for optimization hypotheses, including success metrics, guardrails, and interpretation of results.
- Build causal inference models (including difference-in-differences and synthetic control) when randomized experiments are not feasible.
- Estimate demand elasticity and customer substitution effects to support pricing and allocation decisions.
- Create decision-relevant metrics, dashboards, and narratives that turn analysis into actionable recommendations.
- Present findings and analytical limitations to both technical and executive audiences, support stakeholder confidence, and guide decisions on priority yield initiatives.
- Stay current with industry trends in AI, cloud economics, and optimization techniques.
- Provide mentorship through code reviews, innovation, and sharing best practices.
- Apply knowledge of Azure pricing, cloud economics, and customer workflows to shape recommendations and explain business trade-offs.
- Embody Microsoft’s culture and values.
What you bring
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Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year of data-science experience (for example: managing structured and unstructured data, applying statistical techniques, reporting results).
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OR Master’s in the same fields AND 3+ years of data-science experience (for example: managing structured and unstructured data, applying statistical techniques, reporting results).
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OR Bachelor’s in the same fields AND 5+ years of data-science experience (for example: managing structured and unstructured data, applying statistical techniques, reporting results).
- OR equivalent experience.
Tech stack: Python, SQL, KQL, distributed data-processing systems.
Preferred qualifications
- Doctorate with 3+ years of data-science experience.
- Master’s with 6+ years of data-science experience.
- Bachelor’s with 8+ years of data-science experience.
- Experience with yield or revenue management, pricing optimization, or cloud resource allocation.
Application timeline: Open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
