Microsoft's Commerce Risk Sciences team is seeking an Applied & Data Scientist to automate manual workflows within fraud decisioning systems, aiming to reduce manual intervention and improve risk assessment accuracy.
Requirements
- Hands-on work experience with machine learning frameworks and techniques.
- Applied research or development in areas like LLM fine-tuning, evaluation, or RAG implementations.
- 1+ years experience with feature engineering, model evaluation, and data pipeline design.
- 1+ years experience with model evaluation platforms.
- 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.
Responsibilities
- Develop and maintain low-latency data ingestion pipelines to feed ML models.
- 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.
- 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.
- Coordinate upstream/downstream data needs and manage deliverables via Azure DevOps.
Other
- Ability to meet Microsoft, customer and/or government security screening requirements.
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
- Ability to manage ambiguity and drive clarity in complex, cross-functional environments.
- Demonstrated ability to work cross-functionally with engineering, product, and program management teams.
- Proven track record of delivering scalable and ethical AI solutions.
