Microsoft’s Marketing Engines & Experiences team is a diverse, collaborative, solutions-oriented team committed to planning, activating and fiscally managing all paid media/advertising, strategic partnerships and agency relationships across Microsoft. The team works across business groups to tie objectives, insights and innovation together to drive business impact through world-class media investment and measurement strategies.

We are seeking a highly skilled and experienced Senior Data Scientist - Media Optimization to join our dynamic team. The ideal candidate will have a background in data science, business intelligence, or business and financial analysis, along with significant experience in paid media measurement, optimization

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.

Required/Minimum Qualifications

• Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • OR equivalent experience.
• 5+ years experience building predictive models
• 5+ years experience writing SQL to analyze data.
• 5+ years experience writing code to include: Python, R or comparable languages.

Data Analytics IC4 - The typical base pay range for this role across the U.S. is USD $106,400 - $203,600 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 $137,600 - $222,600 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

Microsoft will accept applications for the role until October 8, 2025.

• Data Preparation and Understanding Prepare and manage data for project plans, proactively detect changes, and communicate updates to senior leads.
• Develop usable data sets, contribute to data ethics and privacy policies, and engage with customers about data integrity and cleanliness.
• Evaluating for Insight and Impact Ensure models align with business goals, evaluate their effectiveness, design feedback methods, mentor engineers, and present findings to stakeholders.
• Industry and Research Knowledge / Opportunity Identification Provide technical feedback to the engineering team, identify business opportunities, propose collaborations, support strategy execution, leverage existing systems, share industry knowledge, and contribute to thought leadership and best practices.
• Modeling and Statistical Analysis Use machine learning solutions and algorithms to identify the best approach for objectives. Understand and apply modeling techniques, manage data quality, and communicate findings to stakeholders.
• Write scripts in SQL, Python, and R.
• Construct hypotheses, design experiments, analyze results, and communicate effectively.
• Develop scalable operational models with data engineering teams.
• Coach engineers on best practices.
• Understand Microsoft's Artificial Intelligence (AI) and Machine Learning (ML) tools.
• Break down complex topics for customers.
• Help the Solution Architect and provide guidance on model operationalization that is built into the project approach using existing technologies, products and solutions, as well as established patterns and practices.
• Embody our culture and values.