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.
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.
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) 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.