Senior Data & Applied Scientist

Job description

**Overview** Microsoft's AI for CELA team is part of Corporate, External, and Legal Affairs (CELA), the organization that brings together legal, regulatory, public policy, compliance, and corporate affairs professionals to help Microsoft navigate complex issues and operate responsibly. The team is seeking a Senior Data & Applied Scientist to apply advanced data science, machine learning, and artificial intelligence to transform how these professionals capture knowledge, manage work, and respond to a rapidly changing regulatory environment. In this role you will identify and frame complex, ambiguous problems; create project plans that account for risks, constraints, assumptions, and available resources; and connect technical measures to meaningful outcomes for legal professionals, business clients, and engineering teams. You will build AI-enabled knowledge-management capabilities that convert fragmented requests, guidance, communications, and work product into structured, governed, and reusable institutional knowledge. The work includes improving intake and triage, enabling high-quality search and summarization, generating draft guidance, extracting insights, and automate decision support into the tools and workflows that CELA professionals use every day. You will also develop AI solutions for continuous regulatory intelligence, including detecting and prioritizing regulatory changes, extracting and structuring requirements, mapping changes to obligations, policies, products, and controls, and producing traceable impact assessments for expert review. Success requires strong technical judgment, partnership with legal and regulatory experts, disciplined evaluation, human-in-the-loop design, clear communication, and a commitment to responsible, secure, and auditable AI. 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. **Responsibilities** • Own complex data science engagements by translating ambiguous legal, knowledge-management, and regulatory problems into clear objectives, project plans, measurable success criteria, and roadmaps that improve outcomes over time. • Acquire, assess, and prepare structured and unstructured data from communications, documents, matters, regulatory sources, and operational systems; identify data-quality, integrity, privacy, security, bias, and ethical risks; and establish reliable data foundations for AI. • Design, develop, and evaluate statistical, machine learning, and generative AI approaches for classification, routing, retrieval, summarization, knowledge drafting, metadata extraction, regulatory monitoring, requirements extraction, and impact analysis. • Write efficient, readable, extensible, production-quality analysis and software code; diagnose complex issues; prototype and operationalize scalable solutions; and partner with engineering teams on deployment, monitoring, maintenance, and continuous improvement. • Define evaluation metrics and human-review workflows that connect technical performance to accuracy, consistency, traceability, adoption, capacity returned, and business value, while ensuring that legal and regulatory judgment remains with accountable experts. • Build trusted partnerships with legal professionals, policy experts, engineers, researchers, and business stakeholders; influence decisions through clear narratives and visualizations; mentor less experienced practitioners; and establish responsible AI, data, and engineering best practices across the team. **Qualifications** Required Qualifications - Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) 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 3+ years 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 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Preferred Qualifications - Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years 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 6+ years 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 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. - Advanced knowledge of statistical analysis, experimentation, algorithms, machine learning, and AI, with experience selecting and applying methods appropriate to the problem, data, and desired outcome. - Experience managing, transforming, and analyzing structured and unstructured data and developing reproducible solutions using SQL or related query languages and Python, R, or another relevant programming language. - Experience developing, evaluating, or operationalizing scalable machine learning or AI systems, including retrieval, natural language processing, large language models, agentic workflows, and the definition of quality and impact metrics. - Demonstrated ability to own complex projects, make sound decisions amid ambiguity, collaborate across legal, policy, research, product, and engineering disciplines, manage competing constraints, and deliver high-quality results. Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. --- Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about [**requesting accommodations.**](https://careers.microsoft.com/v2/global/en/accessibility.html)