Senior Economist

Microsoft•Redmond, WA

2d•$119,800 - $234,700•Remote

About The Position

Within CE&S, the Global Customer Success (GCS) organization is leading the effort to enable customer success on the Microsoft Cloud by combining AI-powered capabilities and human expertise to deliver innovative solutions that accelerate business value, drive operational excellence, and nurture long-term loyalty. We are seeking a Senior Economist who thrives in fast-moving environments and enjoys solving real-world business problems with rigorous, data-driven methods to join the AI Strategy & Business Innovation team (AISBI) within GCS. This role sits at the intersection of economics, analytics, and business strategy, helping teams move from intuition-led decisions to evidence-led action. You will own and shape measurement approaches for complex initiatives, partner across business and analytics teams, and uphold a high bar for statistical quality and decision usefulness. We are looking for someone that is curious, pragmatic, and comfortable operating in ambiguity, someone who can challenge assumptions, translate technical findings into clear recommendations, and influence leaders to act with confidence. This role requires a high degree of independence and self-direction. You should be comfortable operating as a senior individual contributor, building and pressure-testing your own analytical approach, and proactively engaging external expert communities when needed. 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.

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 1+ year(s) of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 3+ years of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 5+ years of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 3+ years of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 5+ years of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics or related field AND 7+ years of experience in applied economics or data-science (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
3+ years of experience applying causal inference in real-world settings
Experience with quasi-experimental methods (DiD, IV, matching, regression techniques), experimentation and impact evaluation
Demonstrated ability to communicate complex findings to business leaders and cross-functional partners, translating technical concepts into executive-ready messages is a critical competency-
Familiarity with portfolio-level or risk modeling concepts, cost modeling under uncertainty
Proven ability to work with messy, observational data and incomplete measurement systems
Experience in SaaS / cloud services / platform economics, marketplace or pricing organizations, or experimentation teams (growth, product analytics)
Exposure to operations, reliability, or capacity planning systems; AI-driven or automation-heavy environments -
Familiarity with customer lifecycle, retention, and growth analytics.
Experience influencing cross-functional teams in ambiguous, fast-moving environments.

Responsibilities

Design and apply causal inference methods to evaluate business initiatives in non-experimental settings.
Identify, define, and quantify customer and business outcomes, then causally evaluate product and service offerings to determine which combinations maximize outcome attainment.
Build robust analytical frameworks that connect customer, commercial, and operational signals to measurable outcomes.
Develop cohort-level economic insights to support prioritization, investment decisions, and long-term planning.
Partner with data and engineering teams to define practical data requirements and improve analytical readiness.
Translate technical findings into clear, executive-ready recommendations.
Raise analytical rigor across the organization through thought partnership, review, and coaching.
Review analytical outputs from internal and external partners for methodological quality and clarity.