Principal Applied Scientist

Ads Fraud Detection sits at the critical intersection of Security and Machine Learning, defending one of the largest advertising ecosystems in the world. We build and operate advanced ML systems that identify and neutralize fraudulent web traffic in real time at massive scale, protecting advertiser ROI, publisher integrity, and user trust. Our mission requires us to push the frontier of high-precision, high-recall detection while adapting continuously to an increasingly sophisticated threat landscape. 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. You will also ensure that every solution meets production reliability requirements. Your work will directly influence platform capabilities. 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.   Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

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.

Required Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field ]  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.