The role
From Relx's own posting.
** Please note that this is an office-based role located in New York City. Team members are expected to work in the office up to three days per week, with in-person attendance driven by customer engagement and business needs. If you are unable to align with this requirement, please consider other opportunities across LexisNexis that may offer greater flexibility. **
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.
Build 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 LangChain, LangGraph, LlamaIndex, OpenAI SDKs, Google ADK, and/or Anthropic/Claude APIs to prototype and refine AI capabilities.
Design the Benchmark & Standard
Design rigorous, domain-aware evaluation methodologies for legal AI — covering accuracy, comprehensiveness, citation grounding, hallucination detection, and other quality dimensions that reflect real legal reasoning.
Define offline and human-in-the-loop evaluation approaches, metrics, and benchmarks that the team can trust and reuse.
Build and run evaluation harnesses to compare models, prompts, retrieval strategies, and configurations, and to track quality and catch regressions over time.
Extend evaluation to long-running, multi-step agents — measuring trajectory quality, tool-use correctness, and the behavior of agent feedback loops, rather than only single-turn outputs.
Partner with engineers to integrate evaluation, monitoring, and observability into production AI applications so quality stays visible after launch.
Balance innovation with practical constraints — latency, cost, reliability, and explainability — when recommending approaches.
Bring others with you
Partner closely with the Applied AI Engineer, machine learning engineers, designers, product managers, legal SMEs, and platform engineering teams. Effective AI product development depends on strong cross-functional collaboration and respect for each discipline’s expertise.
Communicate clearly with people who aren’t data scientists — especially lawyers — and adapt your language to the audience without dumbing things down.
Clearly explain how models work, where they fall short, and what safeguards are in place, to build trust with both technical and non-technical stakeholders in high-stakes environments.
Contribute reusable evaluation methods, datasets, and findings back to shared AI platform capabilities and team practices.
Contribute constructively to technical discussions, collaborate effectively across teams, and remain open to feedback and evolving approaches.
Required qualifications
6+ years of experience as a Data Scientist, Applied Scientist, Machine Learning Engineer, or related quantitative role, with a track record of shipping data-driven solutions.
Strong foundation in statistics and experimental design — hypothesis testing, A/B testing, causal inference, and confidence/uncertainty quantification.
Strong programming skills in Python and its data stack (pandas, NumPy, scikit-learn) plus SQL for querying and shaping large datasets.
Hands-on experience developing and evaluating LLM-powered or machine learning solutions, ideally in production-oriented settings.
Demonstrated ability to design rigorous evaluation methodologies and metrics for AI/ML systems, including offline/online evaluation, error analysis, benchmark construction, and quality measurement.
Practical experience with LLM techniques such as prompting, retrieval-augmented generation (RAG), embeddings and semantic search, and structured generation.
Experience with the full modeling lifecycle: data exploration, feature engineering, model training and validation, and monitoring for drift and degradation in production.
Familiarity with modern AI engineering frameworks and tooling such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, Anthropic APIs, or equivalent systems.
Experience working with AI/ML systems and data infrastructure on AWS, Azure, or GCP.
Ability to translate ambiguous business problems into well-scoped, measurable questions and