Principal Applied Scientist-Ads Monetization

Microsoft•Redmond, WA

23h•$142,800 - $331,200•Hybrid

About The Position

Join Microsoft Monetization to build the future of AI-powered monetization and shape how people discover, choose, and engage with products, services, and brands across Microsoft’s most innovative experiences. Microsoft Monetization is building the next generation of AI-powered advertising and commerce experiences across Search, Shopping, Copilot, and emerging agentic surfaces. As conversational agents increasingly become the interface between users and businesses, we are reimagining how products, services, and ads are discovered, selected, personalized, and delivered. Explore opportunities across our teams and find the role where your expertise can make the greatest impact. Microsoft Search and Audience Network (MSAN)-Principal Applied Scientist In this role you will: Set the science vision and technical strategy for relevance and ranking, user data and intent understanding, personalization, recommendation, and agent-driven commerce. Lead across multiple science areas, influence architecture, research investments, model roadmaps, and execution strategy, and remain deeply hands-on in advancing machine learning innovation. Relevance and Ranking-Principal Applied Scientist This team builds and improves machine learning models that directly shape the customer experience. In this role you will: connect model performance to real-world impact by analyzing product and user data, understand how people interact with the system, identify opportunities to elevate the experience and iterate quickly with product and engineering partners. 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. AI Experiences employees who live within a 50- mile commute of a designated Microsoft Hub in the U.S. 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.

Requirements

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.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
OR equivalent experience.
Extensive experience building and shipping large-scale machine learning systems in search, recommendation, ranking, advertising, commerce, conversational AI, or personalization.
Deep expertise in modern machine learning, including deep learning, transformers, representation learning, retrieval systems, recommendation systems, and foundation models.
Demonstrated experience serving as a technical lead for large-scale cross-organizational initiatives.
Proven ability to translate research innovations into production systems with measurable business impact.
Experience with LLMs, SLMs, multimodal AI, and agentic systems.
Experience in advertising, e-commerce, shopping, recommendation, or marketplace ecosystems.
Experience developing AI-powered assistants, commerce experiences, or personalization platforms.
Experience optimizing distributed training and inference systems on large GPU clusters.
Experience mentoring principal-level engineers, scientists, and technical leaders.

Responsibilities

Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads experiences.
Drive innovation in machine learning technologies including LLMs, SLMs, multimodal AI, retrieval, ranking, personalization, and recommendation systems.
Define and execute the science roadmap for user intent understanding, product understanding, content relevance, and advertiser matching.
Lead end-to-end ML development, including model architecture, training data strategy, evaluation, experimentation, calibration, and production deployment.
Partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems.
Shape the technical vision for future agent experiences, conversational shopping, and AI-assisted commerce scenarios.
Drive measurable improvements in customer satisfaction, engagement, relevance quality, and business outcomes.
Mentor scientists and engineers while raising the technical bar across machine learning, experimentation, and scientific rigor.