Senior Data Scientist III - AI Evaluation and Prompt Engineering

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.

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.

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.

• 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.

• 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 understanding information retrieval / semantic search concepts.

• Excellent written and verbal communication skills — able to present complex ideas simply and persuasively across distributed teams.

• Proven ability to self‑direct
  , learn new tools and concepts quickly, and apply them pragmatically.

• Strong sense of…