Senior Data Scientist

Overview

The Cloud and AI Platforms Monetization organization is a big picture team that encourages a diverse and inclusive culture. We are growth strategists who enable the Microsoft mission by creating durable profit growth through high-impact monetization strategies, packaging, and pricing.

We 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.

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.

Responsibilities

Data 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