Senior Data and Applied Scientist

Overview Imagine shaping the future of local search for millions of users worldwide. At Bing Places, you'll join a team that powers business entity relevance on the search results page. You'll work on cutting-edge tools and metrics that ensure users find the most accurate and meaningful local results. Our team thrives on innovation, leveraging large and small language models, and advanced measurement systems to deliver exceptional quality. As a Data Scientist in Bing Places, you will design new relevance metrics, build labeling pipelines, and fine-tune language models to improve search quality. You'll work on prompt engineering, implement modern language models techniques like Retrieval Augmented Genaration, and create scalable workflows for measurement and evaluation. This opportunity will allow you to: Accelerate your career growth by working on state-of-the-art AI systems. Develop deep expertise in prompt engineering and model tuning. Hone your skills in building robust data pipelines and quality frameworks. 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. Responsibilities Design and implement new relevance metrics to measure and improve local search quality. Develop and optimize LLM/SLM labeling pipelines for high-throughput, consistent quality judgments. Engineer and fine-tune prompts for LLMs to enhance query understanding and classification accuracy. Apply modern LLM techniques such as retrieval-augmented generation for improved grounding and relevance. Build scalable workflows and dashboards for measurement, evaluation cycles, and quality checks. Analyze failure modes and improve prompt rubrics to reduce defect rates and enhance labeling consistency. Collaborate with cross-functional teams to integrate metrics and labeling systems into production environments. Qualifications Required Qualifications Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND relevant data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND relevant data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)  OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND relevant data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Relevant years customer-facing, project-delivery experience, professional services, and/or consulting experience. Other Requirements:Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/