Senior Applied Scientist

Microsoft•Redmond, WA

About The Position

Join the forefront of data center innovation with Microsoft’s Cloud Operations & Innovation Data Center Cloud Applied Artificial Intelligence (CO+I DCAAI) group as an Senior Applied Scientist. In this pivotal role, you’ll play a key part in driving the sustainability and efficiency of Microsoft’s core infrastructure that powers our leading online services, including Bing, Office 365, Xbox, OneDrive, and the Microsoft Azure platform. As a Senior Applied Scientist in DCAAI, you will leverage machine learning, advanced analytics, predictive modeling, and time‑series analysis to transform how Microsoft assesses and manages risk across the lifecycle of its global data center portfolio—from construction through operations. With over 400 data centers in 32 countries and millions of servers, you will help shape the safety, resilience, and reliability of one of the world’s most complex critical infrastructure systems.You’ll partner with cross‑disciplinary experts in engineering, construction, program management, operations, and sustainability to build models that illuminate evolving risks and drive proactive interventions. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. We approach this mission with a growth mindset, innovation, shared goals, and a culture grounded in respect, integrity, accountability, and inclusion.

Requirements

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..
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

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 5+ 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 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.
Experience with large-scale product and service telemetry systems, demonstrating a deep understanding of data collection, processing, and analytical methodologies.
Experience with Exploratory Data Analysis, Inference analysis, Predictive analysis (Regression, Classification, etc.), Time Series Analysis and Forecasting, and Machine Learning (SVMs, Gradient Boosted Trees, Neural Networks, CNNs, etc.).
Foundation in applied statistics and mathematics, with the ability to apply advanced analytical techniques to solve complex problems.
4+ years of experience leveraging the practical uses of statistics (i.e. experimentation, statistical modeling).
2+ years of customer-facing, project-delivery experience.
Experience with Spark and Python.
Must have led a project where you independently framed a complex problem, developed testable hypotheses, and executed a rigorous, evidence‑led investigation to validate the solution.

Responsibilities

Analyze large‑scale telemetry and time‑series data from data center infrastructure developing products improving data center efficiency, sustainability and infrastructure health.
Apply statistical modeling, machine learning, and forecasting techniques to build predictive and prescriptive models that drive actionable outcomes.
Translate complex datasets into clear, actionable insights that improve operational effectiveness, inform long‑term strategy, and enable data‑driven decision‑making across DCAAI.
Design, run, and evaluate controlled experiments (A/B tests, offline evaluations, simulations) to validate hypotheses, test scientific approaches, and ensure rigor, reliability, and reproducibility.
Build and prototype new algorithms and modeling approaches, evaluating trade‑offs in accuracy, interpretability, computational efficiency, and deployment feasibility.
Collaborate closely with engineering teams to productionize and operationalize models at cloud scale, ensuring performance, reliability, and maintainability in real‑world environments.
Partner with product, engineering, and operations stakeholders to identify high‑impact opportunities, scope scientific workstreams, and drive measurable business and sustainability outcomes.
Provide strategic, data‑driven guidance on potential product enhancements, optimization opportunities, and long‑term investment areas.
Contribute to the product roadmap by aligning scientific solutions with organizational priorities and by proposing innovative telemetry‑based capabilities.
Champion best practices in data quality, feature engineering, experimental design, and model governance, ensuring scientific excellence across the team.
Communicate findings effectively through well‑crafted narratives, visualizations, and presentations accessible to both technical and non‑technical audiences.
Embody our culture and values.