Senior Data & Applied Scientist

Full Job Description

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. 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. 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.

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