Data Scientist - Pricing

Microsoft•Redmond, WA

4d•$102,100 - $219,200

About The Position

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 Data Scientist - Pricing to drive yield optimization strategies for Azure infrastructure (e.g., virtual machines). In this role, you will translate revenue, hardware, and capacity data into actionable insights that maximize resource utilization, improve cost efficiency, and enhance customer experience. You will use your advanced analytics, machine learning, causal inference, and visualization skills to influence strategic decisions at scale. 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. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

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.
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.
Experience in Python, R, or similar languages
Experience with Azure Machine Learning (ML) or equivalent cloud-based ML platforms.
Experience working with large-scale data and distributed systems.
Experience with yield or revenue management, pricing optimization, or cloud resource allocation.

Responsibilities

Analyze large-scale datasets to identify patterns, trends, and opportunities for improving yield and efficiency.
Develop machine learning models to optimize resource allocation and pricing strategies.
Partner with business planning, engineering, product management, and finance teams to align yield strategies with business objectives.
Design and execute experiments to validate optimization hypotheses. Build causal inference models (e.g., difference-in-difference, synthetic control) to measure the impact of business decisions.
Develop dashboards and other visuals to monitor key business trends, identify new opportunities, and translate findings to actionable insights.
Stay current with industry trends in AI, cloud economics, and optimization techniques; share insights and best practices internally.
Embody our Culture and Values