Principal Applied Scientist, Advertiser Demand Intelligence

Overview The Demand Intelligence team is 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 that continuously analyze demand, market dynamics, advertiser behavior, campaign performance, and ecosystem trends we help stakeholders identify opportunities, optimize outcomes, understand market dynamics, and make better business decisions at scale. 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 be responsible for identifying transformational opportunities, setting technical vision, and ensuring the platform delivers trustworthy, explainable, and business-impacting intelligence at scale. 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. AI Experiences employees who live within a 50- mile commute of a designated Microsoft Hub in the U.S. 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, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 5+ years of experience with 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 – this could include 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. You should be comfortable selecting and tuning algorithms for regression, classification, clustering, time-series forecasting, etc., and understand 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 a programming language commonly used in ML (Python, etc.), 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 either 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, social sciences, or related settings (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 ML ops (Azure ML, MLflow), including building pipelines that handle streaming or real-time data for immediate insights. Proven track record of innovation and impact – for example, 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 to build consensus for ambitious technical initiatives that span multiple orgs or disciplines. #Mi