Senior Data Scientist III - AI Evaluation and Prompt Engineering

Location: New York

This job is with Lexis Nexis Legal & Professional®, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

** Please note that this position is open to hiring on a remote basis, with strong preference for individuals who are located / willing to work in U.S. eastern time. *
* About Our Team
Lexis Nexis Legal & Professional serves customers in more than 150 countries with over 11,000 employees worldwide and is part of    RELX   , a global provider of information-based analytics and decision tools for professional and business customers.
Within Lexis Nexis,    Intelligize    focuses on empowering legal, compliance, and corporate professionals through advanced access to    SEC filings and corporate disclosure data   . We're a trusted partner for top law firms, corporations, and regulators seeking clarity and insight from complex financial and legal information.
Our company has been a long-time leader in applying    AI and advanced technologies    to the legal market-improving productivity, accuracy, and decision-making across the profession. We're now extending that leadership with    ethical, practical, and powerful generative AI solutions   , using a flexible multi-model approach that leverages the best capabilities from today's top model creators for each legal use case.
At Intelligize, you'll join a team that values curiosity, creativity, and collaboration. We bring together experts in law, data, and technology to build products that transform how legal professionals access and understand information.
About the Role
We're looking for a curious, creative, and practical    Data Scientist who will focus on AI Evaluation and Prompt Engineering    to help drive our next generation of AI-powered legal research tools. This role focuses on applying data science techniques to evaluate and improve large language model (LLM) systems, build data-driven insights, and shape our product roadmap.
You'll work closely with product managers, data engineers, and subject matter experts to make sense of large collections of legal and corporate documents (such as SEC filings). You'll help us ask better questions, run smarter experiments, and turn data into clear, actionable recommendations.
Responsibilities
Evaluate and tune LLM-powered features   , such as prompt optimization, retrieval-augmented generation (RAG) systems, and semantic search performance.

Design and execute experiments    to measure model quality, reliability, and user impact - translating technical findings into product recommendations.

Develop and maintain data pipelines    for evaluating, tracking, and improving system performance (e.g., accuracy, latency, cost, and relevance metrics).

Analyze structured and unstructured datasets    (e.g., product usage logs, document metadata, LLM outputs) to identify patterns, insights, and areas for optimization.

Collaborate with product managers    to translate product goals into measurable data science questions, propose next steps, and inform roadmap priorities.

Provide technical guidance    to data engineers who build and maintain analytics and model evaluation infrastructure.

Communicate results clearly    - through written reports, dashboards, and presentations - to technical and non-technical stakeholders.

Stay current    on emerging practices in applied NLP, LLM evaluation, and data-driven product development, and thoughtfully adapt them to our environment.

Requirements
3-6 years of experience in    data science, applied NLP, or AI product analytics   , preferably within a SaaS or research-heavy product environment.

Strong proficiency in    Python    and data analysis libraries such as    Pandas   ; solid working knowledge of    SQL   .

Ability to design and evaluate    LLM-based systems    (e.g., RAG pipelines, prompt evaluations, output scoring), even if not specialized in deep learning.

Experience with    data exploration, experimentation, and reporting    - from defining metrics to visualizing and interpreting results.

Comfort working with    document-based datasets    (e.g., text corpora, metadata, embeddings) and…