About the job
As a Principal Applied Scientist on the Microsoft Ads Fraud Detection team, you will lead the most complex and ambiguous fraud challenges: designing new anomaly detection approaches to surface emerging attacks early, building robust cross-signal correlation frameworks to define and expand fraud perimeters, and developing next-generation ML models and low-latency pipelines that stop attacks before they scale. A key expectation for this role is to architect scalable solutions end to end, ensuring detection systems remain performant, resilient, and cost-efficient as data volume, attack complexity, and business demands grow. You will also provide technical leadership across the organization by mentoring other scientists, guiding project direction, and raising the bar on scientific rigor and execution quality.
Responsibilities
Independently lead and execute multiple high-impact fraud detection initiatives, turning ambiguous problems into measurable business outcomes.
Develop practical, deployable machine learning (ML) solutions that operate reliably at production scale, balancing detection effectiveness, latency, resilience, operational complexity, and cost.
Translate scientific insights into production impact through rigorous experimentation, validation, rollout, monitoring, and continuous optimization under real-world operating constraints.
Apply advanced ML methods—including anomaly detection, cross-signal analysis, large language models (LLMs), and other modern AI techniques—with clear evaluation frameworks, robust tests, and success metrics to deliver reliable, scalable, production-ready solutions.
Drive technical collaboration across science, engineering, ads, security, and privacy teams to operationalize research and deliver end-to-end fraud detection capabilities.
Raise the technical bar through mentorship, scientific rigor, responsible AI practices, and high standards for quality, reliability, and governance.
Qualifications
Minimum
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ 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 4+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
Preferred
Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
5+ years experience conducting research as part of a research program (in academic or industry settings).
3+ years experience developing and deploying live production systems, as part of a product team.
3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
Extensive industry experience delivering machine learning solutions to production, including technical leadership in complex, ambiguous problem spaces.
Proven track record architecting scalable ML systems and low-latency decision pipelines that operate reliably and cost-efficiently at web scale.
Deep expertise in several of the following areas: statistical machine learning, anomaly detection, fraud and risk modeling, deep learning, large-scale data mining, and causal inference.
Demonstrated ability to set technical direction, influence cross-functional partners, and drive multi-year strategy across research and engineering teams.
Solid software engineering and system design skills, including model operationalization, experimentation frameworks, and production quality standards.
Experience mentoring and developing scientists, with a history of raising scientific rigor and execution quality across teams.
Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts into business impact.
Research impact through publications, patents, or significant internal innovations in fraud detection, anomaly detection, or related ML domains.