Principal Applied Scientist, Advertiser Demand Intelligence

We’re building AI-powered intelligence systems that transform large-scale marketplace and advertiser signals into actionable insights, recommendations, forecasts, and automated decision support for Microsoft Advertising. By combining advanced machine learning, generative AI, and agentic technologies, we continuously analyze demand, market dynamics, advertiser behavior, campaign performance, and ecosystem trends to help stakeholders identify opportunities, optimize outcomes, understand market dynamics, and make better business decisions at scale.

About the role

As a Principal Applied Scientist, you will establish the scientific direction for Demand Intelligence, mentor team members, drive cross-organizational innovation, and translate advances in machine learning, Generative AI, and agentic systems into industry-leading capabilities. You will identify transformational opportunities, set technical vision, and ensure the platform delivers trustworthy, explainable, and business-impacting intelligence at scale.

Responsibilities

• Define and drive the modeling strategy for the advertising recommendations platform, spanning classical machine learning (for analytics on structured data) and generative AI. Set 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, collaborating closely with data engineering leaders.
• Lead the incorporation of LLM-based components for intelligent narrative generation, including guiding prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques to enable the system to 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 align data science efforts with product requirements and timelines.
• Mentor and develop the applied science team, providing technical guidance to 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 (e.g., 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, assess their potential to enhance the platform, and drive the incubation of innovative ideas. Experimentally verify benefits and incorporate promising approaches to maintain technological leadership.

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 end-to-end ownership of complex projects (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, including leading teams of scientists or acting as a key technical decision-maker on cross-discipline projects.
• Extensive hands-on expertise in ML techniques for predictive analytics, pattern recognition, and optimization, including algorithm selection and tuning for regression, classification, clustering, and time-series forecasting.
• Strategic thinking and excellent communication skills—ability to translate business objectives into technical plans and articulate complex AI concepts to senior leadership and non-technical stakeholders.
• Proficiency in programming (e.g., Python) and data infrastructure, including machine learning frameworks (PyTorch, TensorFlow) and familiarity with data pipelines and databases.
• Experience with natural language processing and LLMs, including practical applications of pre-trained LLM APIs or training/fine-tuning NLP models for summarization, question-answering, or conversational interfaces.
• Practical exposure applying LLMs to domain-heavy contexts (e.g., medical, agriculture, social sciences) with adherence to privacy and Responsible AI expectations.
• Solid background in big data and cloud technologies, including experience with Azure or similar platforms (e.g., Azure Synapse, Data Lake, Azure ML, MLflow) for building pipelines that handle streaming or real-time data.
• Proven track record of innovation and impact, such as contributions to AI products, influential research publications, patents, or recognized leadership in the data science community.
• High proficiency in MLOps and AI governance, including automated training, continuous monitoring, and ensuring models meet security, compliance, and ethical standards.
• Excellent cross-organizational leadership—ability to influence and align teams with differing priorities (engineering, sales, marketing) and build consensus for ambitious technical initiatives.

Schedule

Starting January 26, 2026, 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.

Pay

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