Senior AI %26 Data Science Advisor

GENERAL SUMMARY:

The Senior AI & Data Science Advisor serves as a senior technical resource on the application of advanced data science, analytics, and generative artificial intelligence (GenAI) to discovery and data intelligence challenges in disputes and investigations. The role collaborates with other technologists, business professionals, and legal staff to design and deploy sophisticated solutions to client and business challenges.

The Senior Advisor partners closely with case teams, E-Discovery Analytics Advisors, E-Discovery Advisors, and other colleagues to ensure that AI technologies—particularly large language models, agentic systems, and related tooling—are deployed securely, ethically, and in alignment with firm policies, client expectations, outside counsel guidelines, and legal risk requirements.

As a senior member of the department, the Advisor also mentors colleagues, represents E-Discovery Services in firm-wide AI governance and innovation forums, and helps build the documentation and validation record that supports the defensible use of AI in litigation and regulatory matters.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

·         Serve as a senior advisor to case teams on the strategic application of AI-driven and traditional data analysis techniques in support of litigation, investigations, and regulatory matters.

·         Lead the evaluation, design, and deployment of AI-assisted workflows for tasks such as document classification, prioritization, summarization, issue surfacing, data remediation, privilege analysis support, deposition and witness preparation support, and investigative triage, ensuring appropriate human oversight at each decision point.

·         Collaborate with Practice & Business Systems, E-Discovery Analytics Advisors, and E-Discovery Advisors to integrate generative AI features within review platforms, analytics pipelines, document management and knowledge systems, and reporting workflows, while documenting assumptions, limitations, and quality controls.

·         Architect and build custom AI solutions on enterprise development platforms (e.g., Microsoft Foundry and Azure OpenAI Service) —using retrieval-augmented generation, embeddings and vector search, structured outputs, tool use, and agentic and multi-agent patterns built on open standards such as the Model Context Protocol (MCP), all within the firm’s approved security, confidentiality, and data-residency boundaries.

·         Develop and maintain automated scripts, pipelines, dashboards, and reusable components—using Python, SQL, notebooks, APIs, source control, and sound testing and documentation practices—that incorporate both traditional analytics and AI-enabled outputs to support internal operations, case reporting, and process improvement initiatives.

·         Establish and maintain an evaluation discipline for GenAI systems, including benchmark and golden datasets, automated and human scoring, hallucination and citation-accuracy checks, prompt and model version control, regression testing when vendors update underlying models, and cost, latency, and throughput benchmarking to inform platform and matter-level decisions.

·         Assess the risks, benefits, and limitations of emerging AI technologies and features; lead structured proofs of concept and vendor evaluations; and provide practical written guidance and recommendations on their appropriate use in e-discovery and matter-specific contexts.

·         Lead the ongoing enhancement of departmental workflows by identifying opportunities for efficiency, quality improvement, and consistency using AI-augmented tools and methods, and by defining the standards, playbooks, and templates from which the team works.

·         Mentor and provide technical direction to Advisors, analysts, and project team members, and develop and deliver training and enablement materials that build practical AI and analytics fluency among attorneys, paralegals, and professional staff.

·         Serve as a subject-matter expert to E-Discovery Services and Information Systems colleagues, and to firm-wide AI governance, innovation, and information security forums, on data governance, analytics, and AI-related initiatives as needed.

JOB SPECIFICATIONS:

·         Ability to analyze large-scale structured and unstructured datasets—including text-heavy legal collections and modern communications data such as email, Microsoft Teams and Slack messaging, mobile and short-message data (including RSMF), collaboration sites, and cloud repositories—and translate analytical results into clear, defensible recommendations.

·         Deep understanding of large language models, prompt and context engineering, retrieval-augmented generation, embeddings and vector search, fine-tuning and distillation, tool use and agentic orchestration, APIs, machine learning, natural language processing, and generative AI concepts, including model behavior, prompt sensitivity, context-window management, output variability, and evaluation considerations.

·         Hands-on command of enterprise model and agent development platforms (e.g., Microsoft Foundry and Azure OpenAI Service), familiarity with current frontier and open-weight model families (for example, Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, and Mistral), including the practical trade-offs among them for legal-domain tasks.

·         Working fluency with the AI-enabled platforms used across the U.S. legal industry, including e-discovery and investigation platforms (Relativity aiR, Everlaw, DISCO, Reveal, Nuix, OpenText Axcelerate) and legal GenAI assistants (Harvey, Thomson Reuters CoCounsel, Legora, Lexis AI, Microsoft 365 Copilot), together with the integration patterns—APIs, connectors, add-ins, and MCP-based tooling—that connect them to firm systems.

·         Strong knowledge of applied statistics and modeling, including sampling design, confidence levels and intervals, and the recall, precision, and elusion measures used to validate technology-assisted and GenAI-assisted review.

·         Experience creating production-quality code and documenting workflows for reproducibility, using Python, SQL, Git-based version control, and modern development environments and AI coding assistants (for example, Visual Studio Code, GitHub Copilot, Claude Code, or Cursor).  

·         Demonstrated experience applying AI techniques in risk-sensitive, privileged, and confidential environments, with attention to accuracy, bias mitigation, explainability, auditability, and the preservation of privilege and work-product protection.

·         Ability to explain complex technical and statistical concepts persuasively and credibly, in writing and in person, to partners, associates, clients, vendors, opposing counsel, and courts.

·         Advanced data preparation, feature engineering, and data quality assessment skills across diverse data sources.

·         Strong organizational skills with the ability to lead multiple concurrent projects, competing matter deadlines, and shifting priorities with limited day-to-day supervision.

·         High degree of professionalism, discretion, attention to detail, and commitment to providing exceptional internal client service.