Principal Data and Applied Scientist

With the rapid acceleration of AI and the need to deliver trustworthy, high-performing models, Microsoft's Commercial Business & AI (CEAI) Data Science and Applied AI team is driving innovation at scale! Our mission is to advance AI capabilities through rigorous evaluations, fine-tuning, and large-scale experimentation to create intelligent, personalized experiences for customers worldwide. The CEAI Data Science and Applied AI team is seeking passionate AI practitioners and data scientists to join agile, cross-functional teams working on cutting-edge model evaluation frameworks, optimization pipelines, and experimentation platforms that power Microsoft's AI ecosystem.    We are looking for a  Principal Data and Applied Scientist  to join our team! As a member of the Commercial Business & AI (CEAI) Data Science and Applied AI organization at Microsoft, you will help us accelerate the company's own AI transformation. You will have the opportunity to partner directly with the engineering and product management groups responsible for managing the Dynamics 365 applications that power Microsoft's sales, marketing, and support platforms. You will apply advanced analytics, statistical modeling, machine learning and GenAI tools to uncover insights, drive action and deliver innovative solutions for complex business challenges.    This role offers the opportunity to:  Collaborate across a diverse team of data scientists, engineers, and product managers.  Deepen your experience in the evolving Artificial Intelligence (AI) and Machine Learning (ML) landscape.  Drive meaningful impact for thousands of Microsoft sales, marketing and support platform users.   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.

Partner with senior business, engineering, and product stakeholders to translate ambiguous questions about AI impact into well-scoped scientific problems with clear hypotheses, measurable objectives, and agreed definitions of success.    Own the scientific and statistical strategy for measuring the business impact of AI, including the causal and experimental frameworks (A/B testing, quasi-experimental designs, counterfactual and uplift methods) used to separate real impact from correlation.    Define and govern the metric framework that connects AI adoption and usage to downstream business outcomes such as productivity, revenue, retention, and cost, and set the standards for how those metrics are computed, validated, and interpreted across the organization.    Turn problem formulations into executable plans by selecting or creating the appropriate methods, algorithms, and tooling, and by delivering results that are statistically valid, reproducible, and defensible under executive scrutiny.    Write robust, reusable, and extensible code and analytical pipelines that make impact measurement repeatable at scale rather than a one-time analysis.    Develop ML and GenAI models using advanced statistical, machine learning, and LLM techniques, and quantify their incremental value to the business.    Lead the evaluation of GenAI solutions end to end, designing evaluation methodology, diagnosing quality and performance issues, identifying root causes, and recommending fine-tuning, reinforcement learning, or system-level fixes.    Communicate findings, tradeoffs, and levels of confidence to senior leadership in clear business language, and influence investment and prioritization decisions based on the evidence.    Raise the scientific bar across the broader team through design reviews, mentorship, and reusable measurement standards that other data