Principal Applied Scientist

Microsoft • Redmond, WA

7d • $142,800 - $304,200 • Hybrid

About The Position

The Principal Applied Scientist will lead complex and ambiguous fraud detection challenges on Microsoft Ads Fraud Detection, at the intersection of security and machine learning. The role focuses on designing anomaly detection approaches to surface emerging attacks, building cross-signal correlation frameworks to define and expand fraud perimeters, and developing next-generation machine learning models and low-latency pipelines to stop attacks before they scale. The scientist will architect scalable end-to-end solutions that remain performant, resilient, and cost-efficient as data volume, attack complexity, and business demands grow. The position also provides technical leadership through mentoring scientists, guiding project direction, and raising scientific rigor and execution quality, while ensuring solutions meet production reliability requirements.

The team builds and operates machine learning systems that identify and neutralize fraudulent web traffic in real time at massive scale, protecting advertiser ROI, publisher integrity, and user trust. The role applies modern AI and machine learning methods, translates research into reliable production impact, and collaborates across science, engineering, ads, security, and privacy teams. Microsoft employees are expected to work from a designated office at least four days per week beginning January 26, 2026, if they live within the stated commute radius; this expectation is subject to local law and may vary by jurisdiction.

Requirements

• Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, and 6+ years of related experience; or a master's degree in one of these fields and 4+ years of related experience; or a doctorate in one of these fields and 3+ years of related experience; or equivalent experience.
• Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
• Extensive industry experience delivering machine learning solutions to production, including technical leadership in complex, ambiguous problem spaces.
• Proven track record architecting scalable machine learning 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.
• 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, including the ability to translate complex technical concepts into business impact.

Nice To Haves

• Master's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 9+ years of related experience; or a doctorate in one of these fields and 6+ years of related experience; or equivalent experience.
• 5+ years of experience creating publications, such as patents, libraries, or peer-reviewed academic papers.
• 2+ years of experience presenting at conferences or other events in the outside research or industry community as an invited speaker.
• 5+ years of experience conducting research as part of an academic or industry research program.
• 3+ years of experience developing and deploying live production systems as part of a product team.
• 3+ years of experience developing and deploying products or systems at multiple points in the product cycle, from ideation to shipping.
• Research impact through publications, patents, or significant internal innovations in fraud detection, anomaly detection, or related machine learning domains.

Responsibilities

• Independently lead and execute multiple high-impact fraud detection initiatives, turning ambiguous problems into measurable business outcomes.
• Develop practical, deployable machine learning solutions that operate reliably at production scale while balancing detection effectiveness, latency, resilience, operational complexity, and cost.
• Translate scientific insights into production impact through experimentation, validation, rollout, monitoring, and continuous optimization under real-world operating constraints.
• Apply advanced machine learning methods, including anomaly detection, cross-signal analysis, large language models, and other modern AI techniques, using clear evaluation frameworks, robust tests, and success metrics.
• Drive technical collaboration across science, engineering, ads, security, and privacy teams to operationalize research and deliver end-to-end fraud detection capabilities.
• Provide technical leadership by mentoring scientists, guiding project direction, and raising standards for scientific rigor, responsible AI, quality, reliability, and governance.
• Architect scalable end-to-end solutions, including machine learning models and low-latency pipelines, to detect and stop emerging fraud attacks.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal