Data Scientist, Applied AI Solutions

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Summary The Data Scientist, Applied AI Solutions collects, explores, and analyzes large, complex datasets to improve business and client outcomes. The role designs and deploys data models, algorithms, and machine learning solutions; performs custom analyses; and translates business, legal, and regulatory requirements into practical analytic approaches. The position also develops and applies algorithmic testing methods to evaluate AI systems and tools, including bias and accuracy.

Working with lawyers, clients, and cross-functional teams, the Data Scientist, Applied AI Solutions delivers data-driven solutions with measurable business impact.

Location This position can sit in any of our U.S. offices and offers a hybrid work schedule.

Responsibilities Develops and applies algorithmic testing methods to evaluate AI systems, models, and tools for bias, accuracy, reliability, robustness, and performance.

Creates novel testing techniques, metrics, and evaluation frameworks for emerging AI systems and risks when established methods are insufficient.

Translates legal, regulatory, and business requirements into analytic objectives, testable criteria, data specifications, and practical technical solutions.

Works with internal stakeholders, lawyers, and clients to identify opportunities to leverage organizational and client data to address business, legal, investigative, and regulatory needs.

Prioritizes, scopes, and manages multiple concurrent data science projects and client engagements, with accountability for delivery quality, timelines, measurable business outcomes, and commercially valuable solutions.

Designs, builds, deploys, and supports production machine learning models and data science solutions at scale, including solutions for privilege review and related legal workflows.

Designs representative test datasets, test cases, benchmarks, validation protocols, and monitoring approaches; documents methodologies, results, limitations, and recommendations.

Assesses the quality, completeness, representativeness, and accuracy of structured and unstructured data sources and data-gathering techniques.

Uses Python and SQL extensively to build models, data pipelines, testing tools, and analyses.

Manages structured and unstructured datasets in secure enterprise environments and works with Azure AI services or comparable enterprise cloud and data platforms.

Develops custom data models, algorithms, and predictive solutions that improve client outcomes and support data-driven decision-making.

Coordinates with legal, technical, and business teams to implement solutions, monitor outcomes, and refine models and testing methods.

Communicates methods, findings, risks, and actionable insights to technical and non-technical audiences through client-ready analyses, documentation, and presentations.

Establishes and applies best practices for reproducible analysis, model governance, quality assurance, data protection, and reporting.

Supports the delivery of revenue-generating client solutions within consulting or professional-services delivery models.

Other duties as assigned.

Desired Skills Experience in a law firm or other legal-services environment is preferred. Proven experience delivering revenue-generating client solutions with measurable outcomes, managing multiple concurrent client engagements, and operating within consulting or professional-services delivery models. A specialization in machine-learning, artificial intelligence, cognitive science or data science is preferred. Must be self-driven, curious and creative. Experience must include creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, data mining techniques, etc.

Experience with most or all of the following basic data science tools:
Python, R, SQL, SAS, NLTK, Scikit-Learn, Excel, Tableau, Power BI, and Jupyter;
Basic data science concepts: probability, statistics, hypothesis testing, machine learning, natural language processing, predictive modeling, data visualization; and Big data tools:
Hadoop, Azure, etc. Must be adept in agile methodologies and well-versed in machine learning,…