OverviewWe are seeking a Senior Data Scientist to lead end-to-end yield optimization initiatives for Azure infrastructure (e.g., virtual machines, storage). In this role, you will frame ambiguous business problems, translate revenue, hardware, and capacity data into actionable insights, and guide decisions that balance resource utilization, cost efficiency, and customer experience. You will apply analytics, machine learning, causal inference, and visualization to recommend strategies, influence cross-functional decisions, and measure business outcomes. Your work will inform decisions like how we price new products, how we use prices to encourage certain customer behaviors, what promotions we create, etc.
ResponsibilitiesData Analysis
- Write efficient, readable code in Python, SQL, KQL, or similar languages to prepare and analyze large-scale revenue, hardware, and capacity datasets, leveraging distributed data-processing systems to identify pricing and yield opportunities.
Modeling & Yield Optimization
- Select, develop, and validate appropriate statistical and machine-learning approaches for resource allocation and pricing, assessing methodological limitations and both statistical and business significance.
Cross-Functional Collaboration
- Partner with business planning, engineering, product management, and finance teams to define objectives, prioritize analytical work, and align yield strategies with business goals.
- Influence decisions through evidence, communicate trade-offs, and drive alignment on success measures and implementation.
Experimentation & A/B Testing
- Own experiment design and impact measurement for optimization hypotheses, including success metrics, guardrails, and interpretation of results.
- Build causal inference models (e.g., difference-in-differences, synthetic control) when randomized experiments are not feasible.
- Estimate demand elasticity and customer substitution effects.
Insights & Decision Influence
- Develop decision-relevant metrics, dashboards, and compelling narratives that translate analyses into actionable recommendations.
- Present findings and analytical limitations to technical and executive audiences, build stakeholder support, and guide decisions on key yield initiatives.
Thought Leadership
- Stay current with industry trends in AI, cloud economics, and optimization techniques.
- Provide mentorship through code reviews, innovation, and sharing best practices.
Business Acumen
- Apply understanding of Azure pricing, cloud economics, and customer workflows to shape actionable recommendations and explain business trade-offs.
Embody our culture and values.
QualificationsRequired Qualifications- 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)
- OR 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)
- OR 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)
- OR equivalent experience.
Preferred Qualifications - 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)
- OR 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)
- OR 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)
- OR equivalent experience.
- Experience with yield or revenue management, pricing optimization, or cloud resource allocation.
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
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
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
