Principal Applied Scientist – Sales Insights Platform

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<div class="isg-job-description“>OverviewWe are building a scalable, Azure-based analytics platform that delivers rich, customer-level sales insights and proactive alerts to Microsoft’s internal sales and account management teams. This platform provides a comprehensive view of each enterprise customer by surfacing insights like abnormal revenue changes, share-of-wallet opportunities, peer benchmarks, competitive keyword gaps, and product feature adoption health, on a daily and real-time basis.The underlying system leverages traditional machine learning models for data-driven analysis and augments the delivery of insights with agentic interoperation using large language models (LLMs) to generate meeting briefs, pitch assets, followups, and safe CRM updates for our sales agents.As a Principal Applied Scientist, you will define technical vision and lead the development of both the ML and LLM components of this platform, ensuring it scales across millions of data points and delivers trusted, actionable intelligence that drives business decisions. This role is available in either Redmond, WA or Mountain View, CA. 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.
ResponsibilitiesDefine and drive the modeling strategy for the Sales Insights platform, spanning classical machine learning (for analytics on structured data) and the use of generative AI (for insight summarization). You will set the direction on which problems to tackle with ML (e.g. anomaly detection, predictive modeling, 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.

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