Principal Applied Scientist, Advertiser Demand Intelligence

About the role

We are building a large-scale Demand Intelligence platform that transforms complex data into high-quality, rich, actionable insights for Microsoft Advertising stakeholders. The system integrates advanced machine learning models with emerging agentic capabilities powered by large language models (LLMs) to deliver recommendations, automate analysis, generate contextual summaries, and streamline workflows across organizational tools.

As a Principal Applied Scientist, you will define the 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.

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 a 25-mile commute of a non-U.S. 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. 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 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. Guide the development of prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques so 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. Chair technical design reviews, balance priorities, and ensure 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. Establish metrics for success (accuracy, precision of alerts, coverage of insights), monitor model performance in production, and implement processes for periodic retraining, validation, and Responsible AI compliance (addressing bias, fairness, and transparency).
• Stay ahead of emerging trends in AI, whether 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 advanced.

Qualifications

Required Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years of related experience (e.g., statistics, predictive analytics, research)
• OR Master's Degree in the same fields AND 4+ years of related experience
• OR Doctorate in the same fields AND 3+ years of related experience
• OR equivalent experience.

Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years of related experience
• OR Doctorate in the same fields AND 6+ years of related experience
• OR equivalent experience.
• 5+ years of experience developing and deploying machine learning solutions in production, with proven ownership of complex projects end-to-end (from problem formulation and data acquisition to model deployment and monitoring).
• 3+ years of technical leadership experience in an applied science or data science team setting—leading a team of scientists or acting as the key technical decision-maker on cross-discipline projects, with responsibility for delivering major features or systems.
• Extensive hands-on expertise in ML techniques for predictive analytics, pattern recognition, and optimization. Comfortable selecting and tuning algorithms for regression, classification, clustering, time-series forecasting, etc., and understanding their trade-offs.
• Strategic thinking and excellent communication abilities—capable of translating high-level business objectives into technical plans and articulating complex AI concepts and project updates to senior leadership and non-technical stakeholders.
• Proficiency in programming and data infrastructure—solid coding skills in Python or similar languages, experience with machine learning frameworks (e.g., PyTorch, TensorFlow), and familiarity with data pipelines and databases.
• Experience with natural language processing and LLMs—a deep understanding of how large language models can be applied, and practical experience using pre-trained LLM APIs or training/fine-tuning NLP models for tasks such as summarization, question-answering, or conversational interfaces.
• Practical exposure applying LLMs to domain-heavy contexts such as medical/health, farming/agriculture, or social sciences (e.g., domain adaptation, terminology grounding, retrieval-augmented patterns) while adhering to privacy and Responsible AI expectations.
• Solid background in big data and cloud technologies—experience with Azure or similar cloud platforms for big data (Azure Synapse, Data Lake, etc.) and MLOps (Azure ML, MLflow), including building pipelines that handle streaming or real-time data for immediate insights.
• Proven track record of innovation and impact—contributions to significant AI products or platforms, authorship of influential research publications or patents, or recognized leadership in the data science community.
• High proficiency in MLOps and AI governance—experience setting up automated training, implementing continuous monitoring and alerting for model performance, and ensuring models meet security, compliance, and ethical standards.
• Excellent cross-organizational leadership—ability to influence and drive alignment among teams with different priorities (engineering, sales, marketing, etc.), and build consensus for ambitious technical initiatives that span multiple organizations or disciplines.

Pay

The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation.