Senior Legal Data Scientist, AI Innovation

Contact Discovery Services•Washington, DC

9d•$125,000 - $160,000•Remote

About The Position

A leading eDiscovery technology and consulting firm headquartered in Washington, DC is looking for a Senior Data Scientist in the eDiscovery space to focus on designing and implementing client-specific solutions throughout the EDRM. The Legal Data Scientist will be a core part of the Advisory Services Team as they rapidly expand and make key investments in applying Generative AI and Machine Learning (TAR 2.0) across complex disputes, investigations, and litigations. The qualified candidate should have extensive experience with creating workflows supporting complex litigations and investigations. We take a consultative approach in working with our clients and managing cases and an extensive knowledge of the full EDRM, legal case management, and litigation lifecycle is important. This role demands a strong leader who can partner effectively with all levels of the organization as well as outside counsel leading each matter. The Senior Legal Data Scientist, AI Innovation, will be a foundational member of the expanding Advisory Services team. This role is crucial for transforming strategic vision into defensible, client-facing AI solutions within the eDiscovery domain. The ideal candidate will possess deep technical expertise in Generative AI and Machine Learning (ML), combined with the ability to clearly communicate complex concepts to clients and internal stakeholders and an active US bar license.

Requirements

Experience: Minimum of 3 years in a Data Scientist, Machine Learning Engineer, or similar technical role, with significant experience in a regulated industry (Legal, Finance, or Healthcare).
eDiscovery/Legal Tech Proficiency: Demonstrated experience or high familiarity with industry-standard eDiscovery platforms, specifically including RelativityOne’s aIR, eDiscovery AI, or similar advanced analytics tools.
Technical Skills: Expert-level proficiency in prompt engineering, Python, data analysis, and experience working with large language models (LLMs) and advanced ML concepts.
Communication: Proven ability to translate complex technical concepts (e.g., model drift, hallucination, confidence scoring) into clear, non-technical business language for a C-Suite and client audience.

Nice To Haves

Barred attorney in any jurisdiction
Thrive in an environment where innovation meets high stakes (legal defensibility).
Be eager to build something entirely new, moving from zero to one.
Possess a strong, almost consultative mindset when approaching technical problems.

Responsibilities

Technical Strategy & Execution (Focus on ML & Generative AI)
Prompt Engineering & Refinement: Lead the creation, testing, and continuous refinement of advanced Generative AI prompts and strategies (e.g., using tools like RelativityOne’s aiR/eDiscovery AI) to maximize accuracy, efficiency, and defensibility of outputs.
QC Iteration Process: Reviewing all results of each prompt iteration in order to better identify pitfalls and where QC needs to be focused after the use of AI.
AI Auditability & QC Protocol: Assist in the design and implement the technical framework for the AI Auditability Protocol, ensuring all ML and Generative AI outputs are transparently logged and auditable (including full prompt history and system parameters) for legal defensibility.
Model Optimization: Collaborate with the Engineering Team to integrate, test, and customize both Generative and Non-Generative AI models within case specific workflows, ensuring optimal performance and accurate results.
Development of New Technology: Analysis of off-the-shelf tools and make recommendations for new offerings.
Suggest and plan the creation of tools that augment and improve current workflows using Generative and Non-Generative AI applications.
Client Consulting & Education (Focus on Communication)
Expert Consulting: Act as the Subject Matter Expert (SME) in direct consultations with clients and legal counsel, clearly explaining the mechanics, defensibility, and limitations of our AI tools to build trust and drive adoption.
Internal Enablement: Develop and deliver technical training and documentation on AI usage for the Sales and Client Services teams, turning complex concepts into clear, value-driven messages.
KPI Reporting: Support the Managing Director in defining, tracking, and reporting on key AI usage and accuracy metrics (KPIs) to the Partner Team and C-Suite.