Senior Data Scientist

We are seeking a Senior Data Scientist to help shape the Microsoft consumer eCommerce experience by applying data science and emerging AI to some of our most important customer and business opportunities. You will combine advanced analytics, experimentation, machine learning, and innovative technologies to develop solutions that improve how our teams understand customers, generate insights, and make decisions.  

This role sits at the intersection of data science, applied AI, and user-centered innovation. You will partner across business and technical teams to understand key users, scenarios, and decisions, rapidly test new approaches, and translate ideas into scalable capabilities. Success in this role goes beyond building technical models. It means creating analytical solutions that are intuitive, relevant, actionable, and ultimately adopted by the people they are designed to support. 

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. 

• Apply data science techniques to customer and business problems, including machine learning, statistical analysis, experimentation, causal inference, segmentation, forecasting, and predictive modeling.  

• Translate ambiguous business and user needs into clear analytical approaches and scalable solutions.  

• Develop solutions around specific users and decision-making scenarios, making insights easier to discover, understand, and act upon.  

• Explore and apply emerging technologies, including generative AI, large language models, and AI agents, to improve insight generation and analytical experiences. 

• Rapidly prototype and evaluate new approaches, balancing technical proficiency with speed and practical business value.  

• Build and operationalize analytical and machine learning solutions in partnership with engineering and other technical teams.  

• Determine when traditional analytics, machine learning, generative AI, or a combination of approaches is most suited to solve a problem.  

• Evaluate solutions based on both technical quality and real-world effectiveness, including usability, adoption, decision quality, and business impact.  

• Communicate analytical concepts, findings, and recommendations clearly to both technical and non-technical audiences.  

• Develop reusable analytical methods, frameworks, and capabilities that can scale across users and business scenarios. 

Embody our culture and values.