The role
From Relx's own posting.
Would you like to lead a team building production-grade NLP and LLM capabilities that transform legal and business information into actionable intelligence?
Do you enjoy shaping the future of AI and data science in a way that delivers measurable customer and business impact?
***US Based Remote Opportunity***
About our Team :
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).
About LexisNexis Data Sciences:
At LexisNexis, we're redefining legal intelligence through advanced AI. Our teams harness the power of large language models (LLMs), RAG, agentic systems and large-scale structured and unstructured content systems to build tools that acquire, structure, enrich and deliver legal content. Join us in building AI systems that help legal professionals move faster, think deeper, and operate with confidence.
Role Overview:
We are seeking a hands-on Senior Manager of Data Science to lead a high-impact team in developing the strategy, standards, and execution of AI across our content ecosystem. You will lead a team that embeds machine learning and generative AI directly into production systems operating at scale .
Our applied research opportunity balances innovation with practical constraints (e.g. latency, cost, reliability), requiring a strong ability to quickly iterate on prototypes (e.g. “vibe coding”), communicate tradeoffs, and rapidly deploy to production environments.
This role is central to our transformation toward an intelligent, agent-enabled content platform which is capable of grounded reasoning, turning structured and unstructured data sources into legal knowledge.
Key Responsibilities:
Scope & Impact
Set the vision and strategic priorities, acting as a recognized expert for Data Science
Lead and develop a team of data scientists and ML engineers, setting the cultural tone for the group
Drive applied research with a clear path to production , explicitly balancing innovation against real-world constraints including latency, cost, and reliability
Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems to rigorously measure model quality and business impact
Champion hands-on rapid prototyping and iteration
Collaborate with other Data Science teams to maximize re-use of components and patterns, eliminating waste, duplication and unnecessary customization
Operate with broad scope, coordinating across multiple cross-functional teams, systems, and domains
Technical & Product Leadership:
Collaborate closely with other Data Science teams, to define and execute the AI roadmap across the content lifecycle , maximizing reuse in areas including:
Content collection (e.g. “web scraping”) and transformation
Metadata extraction, enrichment, and classification
Agentic workflows turning real-world events and legal content into legal intelligence
AI-powered downstream product capabilities
Design and deploy scalable, production-grade AI systems , including:
LLM-powered document understanding and generation
Agentic workflows balancing agent autonomy and efficiency with required structure and accuracy
Retrieval-augmented generation (RAG) pipelines
Hybrid ML + rules-based systems for structured content
Lead through execution and by example:
Actively writing code, not just delegating
Building and demoing working prototypes (e.g. by “vibe coding”)
Directly contributing to experiments and production models
Establish and scale best practices in Data Science , including:
Model development, evaluation, and monitoring
Prompt engineering and experimentation frameworks
Data preparation and feature engineering standards
Reusable components and platform capabilities
Partner closely with engineering, architecture, and product leaders to:
Integrate AI into large-scale distributed systems
Ensure performance, scalability, and reliability
Align technical solutions with business outcomes
Translate complex, ambiguous problems into clear project plans and executable solutions , and lead teams through delivery
Present tradeoffs, alternative approaches and options when faced with delivery constraints
Team & Operational Excellence:
Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
Establish best practices for evaluation, observability, and responsible use of generative AI.
Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments.
Core Qualifications:
Experience & Education
Advanced degree (Master’s or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred , or equivalent practical experience
Bachelor’s degree in a relevant field with significant applied experience in data science, machine learning, or AI
Typically requires:
8+ years of relevant experience in data science, machine learning, or applied AI
4+ years of leadership experience (direct or indirect team management)
We recognize that exceptional candidates may follow non-traditional paths and value demonstrated impact, technical depth, and leadership over strict credential requirements.
Success in this role requires:
Leading through both technical expertise and organizational influence
Acting as a change agent , embedding best practices into workflows and systems
Driving both team development and strategic outcomes across a broad scope
Ability to select the right tools and technologies to solve business problems
Technical Proficiency
Proficient with Python, ML and LLM tooling such as Google ADK, LangChain, ML Frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques.
Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture.
Strong experience working with structured and unstructured data at scale .
Ability to design and implement data pipelines and preparation workflows.
Experience integrating ML into complex, multi-stage processing systems
Working knowledge of containerization, CI/CD, RESTful API Design and model serving tools.
Cloud infrastructure experience on AWS (preferred), Azure, or GCP.
Familiarity with AI Coding Tools (e.g. GitHub CoPilot, Claude Code, OpenAI Codex)
Preferred Background
Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
8+ years of post-degree experience, with 4+ years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
Familiarity with ethical/legal considerations in deploying generative AI in professional settings.
Work in a way that works for you:
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
Working flexible hours - flexing the times when you work in the day to help you fit everything in an