Applied AI Data Scientist

• Please note that this position will be located in New York City and will require full-time, on-site presence. If you are unable to align with this requirement, please consider other roles across LexisNexis that might allow for hybrid and/or remote work.**

Do you want to help us build further data science capabilities?

And are you eager to work on the quality of data sources serving our end-user products?

About Our Team LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,300 employees worldwide, is apart of RELX, a global provider of information-based analytics and decision tools for professional and business customers.

About The Role LexisNexis Legal & Professional is hiring an Applied AI Data Scientist to help shape the next generation of AI-powered legal products and experiences.

As an Applied AI Data Scientist at LexisNexis, you will partner with internal teams and enterprise stakeholders to design, evaluate, and continuously improve the AI capabilities that power legal research, drafting, and decision-making. You will work directly with AI engineers, machine learning engineers, and product teams to frame problems, run experiments, design evaluation methodologies, and turn applied research into production-ready AI features that support complex legal and professional workflows.

This role sits at the intersection of AI experimentation, developer enablement, evaluation, and customer engagement. You will partner closely with the Applied AI Engineer and the broader Product, Engineering, and AI Platform teams — bringing the data science lens to model selection, retrieval quality, prompting strategy, and measurement so the team builds the right things and knows when they are working.

This is a deeply hands-on role focused on experimentation, evaluation, prototyping, and iterating on AI-powered experiences. The ideal candidate combines strong applied data science and machine learning fundamentals with practical experience working with LLMs, Agentic systems, and AI-native workflows in production-oriented settings.

What You’ll Do Start with customers

• Spend real time with lawyers, legal operations teams, and our internal subject-matter experts — in their offices, on their calls, watching their workflows. Develop a strong understanding of customer workflows and operational challenges through direct engagement.
• Translate ambiguous, half-formed customer pain into well-scoped, measurable problem statements the team can build and evaluate against.
• Collaborate closely with customers and internal stakeholders to prototype, validate, and refine AI-powered workflows and user experiences based on customer feedback and observed user needs.
• Bring the customer voice back into our model choices, evaluation criteria, and the trade-offs we make.
• Occasional travel to customer sites may be required to better understand workflows and gather product feedback.

Bui ld Next Generation of AI

• Design and run experiments that turn applied research and emerging techniques (LLMs, RAG, retrieval and ranking, multi-step reasoning, agent patterns, evaluation science) into validated capabilities for legal use cases.
• Develop and iterate on LLM-powered approaches such as prompt engineering, retrieval strategies, context management, structured generation, and lightweight agent patterns, in collaboration with AI and machine learning engineering teams.
• Design and prototype agentic AI systems — including long-running, autonomous agents that plan, call tools, and reason over multiple steps — and orchestrate them to support complex, multi-stage legal workflows.
• Build the harness around these agents — the orchestration, state and context management, tool integration, and feedback loops that let long-running agents run reliably, recover from errors, and improve over time.
• Build rapid, runnable prototypes to test ideas, de-risk assumptions, and explore UX and architectural trade-offs before formal engineering investment — favoring tangible artifacts over slideware.
• Analyze model and pipeline behavior — error analysis, failure modes, and data quality issues — and turn those findings into concrete, prioritized improvements.
• Contribute meaningful, production-oriented code (not just exploratory notebooks) and partner with engineers to harden promising prototypes for production.
• Work with modern AI tooling and frameworks such as Lang…