Senior Data Scientist Job at Microsoft Corporation (Redmond)

Job Description

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 and digital experiences. 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 impact, and accelerate innovation in media and discovery strategy. To support this transformation, we are seeking a Senior Data Scientist to lead advanced experimentation and causal inference efforts that enable rigorous campaign measurement and drive data-informed marketing decisions at scale. In this role, you'll directly support Microsoft's mission by enabling data-driven marketing decisions that drive customer engagement and revenue growth.

Job Responsibility

Design and implement advanced experimentation frameworks including Bayesian hypothesis testing and causal inference modeling.
Develop heterogeneous treatment effect analyses to understand differential campaign impacts across customer segments.
Establish clear linkage between A/B test results and desired business outcomes.
Assess the degree to which campaigns meet business objectives and address gaps.
Ensure alignment on definitions and standards across stakeholders.
Define, design, and promote appropriate feedback and evaluation methods. Present results and findings to senior stakeholders.
Build and deploy ML models to support portfolio impact measurement and campaign analytics at scale.
Develop and maintain data pipelines that enable scalable, automated analysis.
Evaluate the viability of automated methods for data collection, reporting, and analysis.
Ensure solutions are reusable, easily discoverable, and self-service oriented.
Identify and promote methods that enhance efficiency in analytics and reporting.
Drive meaningful data interpretation to inform business decisions and shape organizational understanding through compelling storytelling.
Recommend and socialize optimal methods for operationalizing, sharing, and scaling insights.
Share knowledge and practical rationale for transitioning ad-hoc analyses to regular reporting features.
Share domain knowledge to create clarity and ensure readiness to leverage data and insights effectively.
Leverage working relationships within and across teams to ensure alignment and quality execution.
Drive the adoption of recommended data sources and analysis practices.
Consult across teams and influence data strategy, experimentation culture, and measurement frameworks on decisions related to data sourcing, analyses, and interpretation of results.
Coach and mentor less experienced analysts, as needed, and enable cross-functional partners to become data-savvy.
Share insights and analytical experience through various means, including dashboards, reports, and visualizations.
Synthesize and simplify details across analyses to highlight relevant findings. Identify opportunities to improve the efficiency of insights reporting techniques.
Guide others and establish partnerships with stakeholders to ensure results are accessible and relevant.

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) experience in data science, applied statistics, machine learning, or quantitative research
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years experience in data science, applied statistics, machine learning, or quantitative research
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years experience in data science, applied statistics, machine learning, or quantitative research
OR equivalent experience.
4+ years of experience with Python or R for statistical analysis and machine learning.
4+ years of experience with SQL and data pipeline development.
3+ years of experience designing and analyzing A/B experiments or causal inference studies.
2+ years of experience with Bayesian methods, causal inference techniques (e.g., difference-in-differences, instrumental variables, propensity score matching), or heterogeneous treatment effect estimation.
2+ years of experience building and deploying ML models.
4+ years of experience presenting complex technical findings to both technical and non-technical senior level stakeholders.