Principal Applied Scientist

Microsoft•Redmond, WA

17h•$142,800 - $304,200•Hybrid

About The Position

We are seeking a Principal Applied Scientist to lead the next generation of click-through-rate (CTR) for Microsoft Advertising. This is a high-impact role responsible for advancing large-scale ranking models that power Microsoft Advertising, generating billions of impressions and revenue-critical decisions daily. You will combine deep machine learning expertise, solid engineering execution, and business intuition to modernize our prediction stack, drive model innovation, and mentor a growing team of scientists and engineers. This role is ideal for someone who thrives in complex, high-scale systems, who brings thought leadership to ML strategy, and who raises the bar for engineering rigor, curiosity, and business-driven decision making across the team. 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.

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 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.

Responsibilities

Lead the end-to-end development of large-scale CTR and other user response signal models for Search and Display ads.
Design, prototype, and ship cutting-edge ML architectures (deep models, multi-task, transformer-based, LLM-assisted, multimodal).
Define long-term modeling strategy and roadmap with clear business impact.
Modernize our current modeling pipelines, addressing critical technical debt in data flows, training pipelines, and inference systems.
Partner closely with engineering teams to improve reliability, monitoring, and performance of distributed training and online serving.
Introduce best practices for experiment design, ablations, feature validation, and productionization.
Work with PMs, monetization teams, and auction experts to translate business needs into modeling goals.
Own model performance holistically: quality, stability, latency, and revenue impact.
Develop frameworks to better understand advertiser value, user behavior, and marketplace dynamics.
Mentor and up-level applied scientists and ML engineers across the organization.
Drive a culture of curiosity, deep system understanding, and high-quality scientific reasoning.
Improve collaboration norms, documentation quality, and cross-team alignment.
Leverage and influence LLM-based tooling (e.g., agents, copilots) to improve team productivity and model development velocity.
Identify opportunities to incorporate new modeling signals, architectures, or evaluation metrics.