Principal Applied Scientist, Advertiser Demand Intelligence

** Overview*
* We are building large scale, Demand intelligence platform that transforms complex data into high-quality and rich actionable insights to Microsoft Advertising stakeholders. The system integrates advanced machine learning models with emerging agentic capabilities powered by large language models (LLMs) to model recommendations, automate analysis, generate contextual summaries, and streamline workflows across organizational tools. As a Principal Applied Scientist, you will define scientific vision and lead the development of both ML and LLM components.

This includes designing robust models, driving experimentation, ensuring statistical rigor, and guiding the platform's evolution toward greater automation, accuracy, and scalability. You will partner closely with engineering and product teams to translate research into reliable production systems, influence long-term strategy, and deliver intelligence that enables smarter, faster, and more informed decisions.

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.

** Responsibilities*
* + Define and drive the modeling strategy for the advertising recommendations platform, spanning classical machine learning (for analytics on structured data) and the use of generative AI. You will set the direction on which problems to tackle with ML (e.g. predictive modeling, anomaly detection, clustering) and how to leverage LLMs to maximize user understanding and value.

+ Architect end-to-end machine learning pipelines - oversee the design of data processing workflows, feature stores, model training/validation routines, and deployment mechanisms that can reliably produce daily insights for all customers. Ensure these pipelines are scalable, efficient, and maintainable, working closely with data engineering leaders on implementation.

+ Lead the incorporation of LLM-based components for the platform's intelligent narrative generation. This includes guiding the development of prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques so that the system can answer complex sales questions and explain insights in conversational language.

+ Oversee cross-team initiatives and collaboration, coordinating with engineering, program management, and stakeholder teams. You will chair technical design reviews, balance priorities, and guarantee that the data science efforts align with product requirements and timelines.

+ Mentor and develop the applied science team, providing technical guidance to other scientists and engineers. Champion best practices in experimentation, coding, and MLOps, and foster a culture of scientific excellence and continuous learning.

+ Ensure robust evaluation and governance of all AI/ML solutions. You will establish metrics for success (accuracy, precision of alerts, coverage of insights), closely monitor model performance in production, and implement processes for periodic retraining, validation, and Responsible AI compliance (addressing bias, fairness, and transparency).

+ Stay ahead of the curve by tracking emerging trends in AI, whether it's new algorithms in anomaly detection or breakthroughs in large language models, and assess their potential to enhance the platform. Drive the incubation of innovative ideas, experimentally verify their benefits, and incorporate promising approaches to keep the platform technologically ahead and highly effective.

** Qualifications*
* *
* Required 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.

*
* 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)

+ OR Doctorate in Statistics, Econometrics,…