Senior Data Scientist

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 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  this role, you'll directly support Microsoft's mission by enabling data-driven marketing decisions that drive customer engagement and revenue growth. Shape What You Will Do As a Senior Data Scientist on the Media Data Science and Analytics team, you will design and implement advanced experimentation frameworks including Bayesian hypothesis testing, causal inference modeling, and heterogeneous treatment effect analysis, while building and deploying Machine Learning (ML) models and data pipelines to support portfolio impact measurement and campaign analytics s will include developing solutions that are reusable, readily discoverable by decision makers, and self-service oriented, driving meaningful interpretation of data, and supporting campaign optimization.

This candidate thrives in fast-paced environments, demonstrates comfort with ambiguity, and collaborates effectively across teams and functional boundaries. Curiosity, a bias for action, and the ability to balance business context with technical experience are essential. In this role, you will support the rhythm of business by providing actionable recommendations to stakeholders based on rigorous analysis and clear insights. You will leverage statistical and machine learning techniques to develop scalable, efficient, and impactful data products while also diving deep into the details to uncover the story behind the data.

You will bring sharp analytical thinking, communication skills, and the confidence to engage with executives and stakeholders across the business.

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.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Responsibilities

• Experimentation and Causal Inference 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.
• Orchestration and Collaboration 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,…