Member of Technical Staff - Data Scientist

As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad — to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It’s also inclusive: we aim to make AI accessible to all — consumers, businesses, developers — so that everyone can realize its benefits.   

Microsoft AI (MAI) is seeking an experienced Data Scientist to help build the next wave of capabilities of our personal AI, Copilot. We’re looking for someone who possesses technical prowess, a methodical approach to problem-solving, proficiency in data science technologies, and a mastery of templating to architect solutions that stand the test of time and who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective. The Growth Data Science team is responsible for driving insights to build great products and guide business decisions using statistical analysis, machine learning, experiments, data mining, and data storytelling. 

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.     

By applying to this Mountain View, CA position, you are required to be local to San Francisco area and in office 3 days a week.    

Required 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.
• 5+ years of experience in leveraging complex data, applied data science, developing sophisticated algorithms, executing large-scale A/B testing, and possessing extensive product knowledge.   
• Experience with creating metrics, predicting trend analysis, measuring traffic patterns, and assessing experimentation results. 
• Proficiency in using one or more programming or scripting languages such as Python, R, or C# to work with data.

Preferred Qualifications:  

• Previous experience in growth engineering or a growth-oriented role.
• Proven track record of driving key growth metrics, including acquisition, engagement, and retention, with concrete examples of initiatives built and shipped.
• Experience building data pipelines to support analytics and experiment scenarios.
• Experience working on product analytics to drive product improvements.
• Commitment to writing clean, maintainable, and well-documented code with a focus on application quality, performance, and security.
• Strong interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers, while clearly communicating complex technical concepts to both technical and non-technical stakeholders.
• Enthusiasm for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in web development and AI.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines.

Data Science IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 per year.  

Data Science IC5 - The typical base pay range for this role across the U.S. is USD $137,600 - $267,000 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 $180,400 - $294,000 per year. 

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

Microsoft will accept applications and processes offers for these roles on an ongoing basis.  

#copilot 

• Drive product insights, opportunity analysis, and track metrics to support efforts across Microsoft Copilot. 
• Drive new ways of instrumentation and measurement approach to evaluate new feature performance through experimentation 
• Define metrics and build basic data pipelines to enable A|B experimentation for new features. 
• Hands-on analysis of large volumes of telemetry data using various algorithms and tools including your own 
• Articulate insights, storyboard with data and communicate to influence leadership and other key decision makers 
• Find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.    
• Enjoy working in a fast-paced, design-driven, product development cycle.   
• Work collaboratively with our engineers, Product Managers, and marketing to take ambiguous projects that drive user growth, engagement, and retention. This includes identifying market opportunities, optimizing app flows and improving product features and proposing innovation solutions based on data. 
• Embody our  and .