Applied Scientist II - ML

If you have an interest in data and are committed to developing systems that enhance informed decision-making at Microsoft, the Insights, Data Engineering & Analytics team (IDEAS) offers a platform for professional growth. 

This team plays a crucial role in supporting Microsoft 365 and the Experiences + Devices group (E+D), both of which are central to the Company's mission to empower individuals and organizations to achieve more. Our objective is to foster a data-informed culture by equipping the Experiences + Devices group with actionable insights to guide their decisions. This initiative represents a significant opportunity to deliver impactful information that will improve operational efficiency, drive empowerment, and support Microsoft’s success in the evolving AI landscape. 
 
As a Applied Scientist II - ML on IDEAs team, you will be working on analysis and modeling to understand the customer journey throughout the end-to-end lifecycle and identify potential opportunity to drive product growth and revenue. This role offers the opportunity to develop experience in designing, prototyping, implementing, and deploying descriptive and predictive models, forecasting, causal inference models, Large Language Models (LLMs) and generative AI frameworks. You will thrive in a collaborative team environment that values cross-functional partnerships and shared success. 
 
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. 

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
• 2+ years of experience with R, Python implementing statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment. 
• 2+ years with experience in synthesizing insights and presenting complex ML model recommendations to technical and non-technical audiences. 
• 1+ year of experience in the ability to structure unscoped problems, define success metrics, and drive execution under uncertainty. 

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 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 $131,400 - $215,400 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 19, 2025.  

• Use machine learning algorithms that structures, analyzes, and uses data in product and platforms to train algorithms for scalable artificial intelligence solutions before deploying while under the direction of a team member.
• Collaborate to leverage data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem.
• Use statistical analysis tools for evaluating Machine Learning models and validating assumptions about the data while also reviewing consistency against other sources.
• Begin to independently run basic descriptive, diagnostic, predictive, and prescriptive statistics. Assists with the communication of insights under the direction of team members.
• Leverage or designs and uses machine learning/data extraction, transformation, and loading (ETL) of pipelines (e.g., data collection, cleaning) based on data prepared.
• Gain expertise in one or more subareas of machine learning, gains understanding of a broad area of research (e.g., Machine Learning, Natural Language Processing, Statistical Modeling, Causal Inference), and understands the corresponding literature and applicable research techniques.
• Use understanding of approaches to identify techniques and seeks feedback from team members.
• Understand and follows ethics and privacy policies when executing research processes and/or collecting data/information.