# Principal Applied Scientist **Company**: Microsoft **Location**: Mountain View **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Salary**: $139,900 - $274,800 per year **Category**: Engineering **Industry**: Technology **Ticker**: MSFT **Wikidata**: https://www.wikidata.org/wiki/Q2283 **Apply**: https://microsoft.ai/job/principal-applied-scientist-42/ **Canonical**: https://yubhub.co/jobs/job_f863c53d-b02 ## Description 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. Responsibilities: ML / Modeling Leadership - 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. Technical & Engineering Execution - 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. Business & Product Impact - 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. Leadership & Mentorship - 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. Innovation & Tooling - 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. Qualifications - 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) - 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) - Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) - Equivalent experience. Additional or preferred qualifications: - 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) - Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) - 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. ## Skills ### Required - Statistics - Econometrics - Computer Science - Electrical or Computer Engineering - Machine Learning - Deep Learning - Transformer-based models - LLM-assisted models - Multimodal models - Experiment design - Ablations - Feature validation - Productionization ### Nice to have - Leverage and influence LLM-based tooling - Identify opportunities to incorporate new modeling signals - Architectures or evaluation metrics