Applied & Data Scientist

The Commerce Risk Applied Science team is seeking a highly skilled and proactive Applied & Data Scientist to join our team, focused on automating manual workflows within our fraud decisioning systems. This role is pivotal in driving technical innovation, reducing manual intervention, and improving risk assessment accuracy across our platforms.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.

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 experiene
• 1+ year(s) Experience with feature engineering, model evaluation, and data pipeline design.
• 1+ year(s)Experience with model evaluation platforms.

 Preferred Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate 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 equivalent experience.
• 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
  Familiarity with manual review workflows and fraud detection systems.
• Prior exposure to vendor data integration and cost-efficiency initiatives.
• Proven experience in applied data science, preferably in risk, fraud, or automation domain
• Ability to manage ambiguity and drive clarity in complex, cross-functional environments.
• This experience should include:
  • Hands-on work with machine learning frameworks and techniques.
  • Applied research or development in areas like LLM fine-tuning, evaluation, or RAG implementations.
  • Demonstrated ability to work cross-functionally with engineering, product, and program management teams.
  • Proven track record of delivering scalable and ethical AI solutions.

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 8, 2025.

API Integration & Engineering

• Familiarity of Design and integration of third-party APIs
• Develop and maintain low-latency data ingestion pipelines to feed ML models

Feature Engineering & Model Development

• Transform raw API responses into usable model features via middle-layer DAs.
• Collaborate with evaluation teams to invoke and assess models
• Develop, deploy, and maintain machine learning models at scale

Data Analysis & Experimentation

• Conduct comparative experiments across APIs to identify valuable data fields and estimate event volumes.
• Analyze manual review cases to define automation scope and edge-case handling strategies.

Cross-Team Collaboration

• Act as the primary point of contact for data science inquiries related to the manual workflow automation
• Coordinate upstream/downstream data needs and manage deliverables via Azure DevOps.

Strategic Impact

• Align technical execution with business goals, including reducing vendor reliance and savings in manual effort.
• Contribute to achieving maximum automation of manual workflows in Azure, Office, and Consumer business