Job Description

Overview

We're building a Frontier Marketing organization where the Media Data Science & Analytics team leads the way in transforming how Microsoft measures, analyzes, and optimizes media investments. Our team blends advanced analytics, experimentation, and AI-powered insights to drive smarter decision-making and measurable business outcomes across paid media and owned digital properties.

We operate with agility, prioritize outcomes over activity, and embrace rapid learning loops to unlock deeper audience understanding, maximize campaign impact, and accelerate innovation in media strategy.

To support this transformation, we are seeking a Senior Data Scientist to help us measure the incremental impact of advertising spend and use that to help our media planning partners optimize media campaigns.

Marketing data science is inherently challenging: data is often observational, incomplete, biased, or limited in scale, and outcomes unfold over time across complex systems. The successful candidate will be someone who can apply rigorous causal methods, exercise sound statistical judgment, and translate uncertainty into actionable insights that inform high-stakes investment decisions.

We’re building a Frontier Marketing organization where the Media Data Science & Analytics team leads the way in transforming how Microsoft measures, analyzes, and optimizes media investments. Our team blends advanced analytics, experimentation, and AI-powered insights to drive smarter decision-making and measurable business outcomes across paid media and owned digital properties. We operate with agility, prioritize outcomes over activity, and embrace rapid learning loops to unlock deeper audience understanding, maximize campaign impact, and accelerate innovation in media strategy. To support this transformation, we are seeking a Senior Data Scientist to help us measure the incremental impact of advertising spend and use that to help our media planning partners optimize media campaigns. Marketing data science is inherently challenging: data is often observational, incomplete, biased, or limited in scale, and outcomes unfold over time across complex systems. The successful candidate will be someone who can apply rigorous causal methods, exercise sound statistical judgment, and translate uncertainty into actionable insights that inform high-stake investment decisions.

Responsibilities

Causal Measurement & Business Impact

• Design and apply causal inference approaches (e.g., quasi-experimental methods, incrementality testing, observational analysis) to estimate the true impact of media investments in settings where randomized experiments may be limited or infeasible.
• Evaluate the effectiveness of marketing strategies while explicitly accounting for data limitations, confounding, selection bias, and uncertainty.
• Translate complex causal findings into clear, decision-oriented narratives for senior marketing and business stakeholders.

Modeling, Statistics & Analysis

• Apply advanced statistical techniques and machine learning where appropriate, with a bias toward interpretability and causal validity over purely predictive performance.
• Balance methodological rigor with pragmatism, selecting approaches that are fit for purpose given the data and business context.
• Write high-quality analytical code (Python, SQL) to support reproducible research, exploratory analysis, and ongoing measurement efforts.
• Identify opportunities to improve measurement approaches, challenge existing assumptions, and introduce best practices grounded in both academic research and industry experience.

Data Understanding & Stewardship

• Prepare, validate, and analyze complex marketing datasets, identifying data quality issues, structural changes, and limitations that materially affect inference.
• Communicate data risks, constraints, and implications proactively to senior partners, ensuring conclusions are appropriately scoped and caveated.
• Uphold high standards for data ethics, privacy, and responsible use, with careful attention to how data is collected, modeled, and interpreted.

What Success Looks Like

• Media investment decisions are better informed by clear, credible causal insights rather than surface-level correlations.
• Stakeholders understand not only what the data suggests, but how confident we are and why.
• Analytical recommendations appropriately reflect data constraints and uncertainty, earning trust through transparency and rigor.
• The team consistently applies causal thinking to difficult, ambiguous marketing problems, even when the data is imperfect.

Qualifications

Required/Minimum Qualifications

• 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.

Preferred Qualifications

• 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 6+ 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 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 5+ years’ experience building ML models.
• 5+ years’ experience writing SQL to analyze data.
• 5+ years’ experience writing code in Python.
• 3+ years’ communicating complex technical concepts to non-technical partner teams.  
• 1+ years’ experience performing causal inference
• 1+ years’ experience with media / marketing data science

Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.