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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
Job Responsibility
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
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. Additional or preferred qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience
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.
Nice to have
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience
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.