M
Not Specified Permanent

Redmond, Washington · USA job

Senior Data & Applied Scientist

Microsoft

Redmond, Washington

Job description

Overview

In this role you apply advanced data science, ML, and AI to transform how CELA professionals capture knowledge, manage work, and respond to regulatory changes. You will build AI-enabled knowledge-management capabilities and support continuous regulatory intelligence, partnering with legal, policy, and engineering teams. The work emphasizes responsible, secure, and auditable AI with a strong focus on impact and governance. You'll translate ambiguous problems into actionable plans and deliver measurable improvements.

Responsibilities
  • Own complex data science engagements by translating ambiguous problems into objectives, plans, success criteria, and roadmaps
  • Acquire and prepare structured and unstructured data from diverse sources while flagging data-quality, privacy, security, bias, and ethical risks
  • Design, develop, and evaluate ML and AI approaches for classification, retrieval, summarization, drafting, and regulatory monitoring
  • Write production-quality analysis and code, prototype scalable solutions, and collaborate with engineering on deployment and maintenance
  • Define evaluation metrics and human-review workflows linking technical performance to business value and legal judgment
  • Build partnerships with legal, policy, research, product, and engineering teams; mentor colleagues; promote responsible AI practices
Key requirements
  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, OR CS with 1+ years of data-science experience OR
  • Master's in the same fields with 3+ years of data-science experience OR
  • Bachelor's in the same fields with 5+ years of data-science experience OR
  • equivalent experience
  • strong collaboration across legal, policy, research, product, and engineering
  • clear storytelling and visualization to influence decisions
  • mentorship and guidance to less experienced practitioners
  • statistical analysis, experimentation, and method selection
  • machine learning and AI, including NLP, LLMs, retrieval
  • data management for structured and unstructured data

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