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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. You will partner with internal teams and enterprise stakeholders to design, evaluate, and continuously improve AI capabilities that power legal research, drafting, and decision-making. This role sits at the intersection of AI experimentation, developer enablement, evaluation, and customer engagement, bringing a data science lens to model selection, retrieval quality, prompting strategy, and measurement.

Responsibilities

Develop a strong understanding of customer workflows and operational challenges through direct engagement.
Translate ambiguous customer pain points into well-scoped, measurable problem statements.
Prototype, validate, and refine AI-powered workflows and user experiences based on customer feedback.
Bring the customer voice into model choices, evaluation criteria, and trade-offs.
Design and run experiments to turn applied research and emerging techniques into validated capabilities for legal use cases.
Develop and iterate on LLM-powered approaches such as prompt engineering, retrieval strategies, and context management.
Design and prototype agentic AI systems, including long-running, autonomous agents.
Build the orchestration, state and context management, tool integration, and feedback loops for agents.
Build rapid, runnable prototypes to test ideas and explore UX and architectural trade-offs.
Analyze model and pipeline behavior, identifying and prioritizing improvements.
Contribute production-oriented code and partner with engineers to harden prototypes.
Work with modern AI tooling and frameworks such as LangChain, LangGraph, LlamaIndex, and various API SDKs.
Design rigorous, domain-aware evaluation methodologies for legal AI.
Define offline and human-in-the-loop evaluation approaches, metrics, and benchmarks.
Build and run evaluation harnesses to compare models, prompts, and configurations.
Extend evaluation to long-running, multi-step agents.
Integrate evaluation, monitoring, and observability into production AI applications.
Balance innovation with practical constraints like latency, cost, reliability, and explainability.
Partner closely with Applied AI Engineers, machine learning engineers, designers, product managers, and other stakeholders.
Communicate clearly with non-data scientists, adapting language to the audience.
Explain model functionality, limitations, and safeguards to build trust.
Contribute reusable evaluation methods, datasets, and findings to shared AI platform capabilities.
Contribute constructively to technical discussions and collaborate effectively across teams.

Requirements

6+ years of experience as a Data Scientist, Applied Scientist, Machine Learning Engineer, or related quantitative role.
Strong foundation in statistics and experimental design.
Strong programming skills in Python and its data stack (pandas, NumPy, scikit-learn) plus SQL.
Hands-on experience developing and evaluating LLM-powered or machine learning solutions.
Demonstrated ability to design rigorous evaluation methodologies and metrics for AI/ML systems.
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.
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.
Communicate findings clearly to engineering, product, and business stakeholders.
Comfortable working in evolving environments and collaborating across teams.
Experience in legal technology, enterprise SaaS, compliance, financial services, healthcare, or other regulated industries (preferred).
Experience with agentic workflows, multi-step reasoning, or tool-calling systems (preferred).
Familiarity with retrieval and ranking optimization, hybrid search, or knowledge graph integration (preferred).
Experience with human-in-the-loop evaluation, annotation workflows, or building internal benchmarks (preferred).
Experience with AI guardrails, hallucination detection, responsible AI, or grounded/citation-based generation (preferred).
Familiarity with fine-tuning or model adaptation techniques (preferred).
Experience contributing to AI copilots, AI assistants, or workflow automation systems (preferred).
Open-source contributions, technical blogging, conference speaking, or AI/ML community involvement (preferred).

Skills

Python
Pandas
NumPy
Scikit-learn
SQL
LLM
Prompt Engineering
Retrieval-Augmented Generation (RAG)
Embeddings
Semantic Search
Structured Generation
Agentic Systems
LangChain
LangGraph
LlamaIndex
OpenAI SDKs
Google ADK
Anthropic/Claude APIs
AWS
Azure
GCP
Statistics
Experimental Design
Hypothesis Testing
A/B Testing
Causal Inference
Confidence/Uncertainty Quantification
Model Selection
Retrieval Quality
Prompting Strategy
Measurement
Evaluation Methodologies
Prototyping
Iterating on AI-powered experiences
Full Modeling Lifecycle
Data Exploration
Feature Engineering
Model Training
Model Validation
Monitoring for Drift and Degradation
AI Engineering Frameworks
AI/ML Systems
Data Infrastructure
Retrieval and Ranking Optimization
Hybrid Search
Knowledge Graph Integration
Human-in-the-Loop Evaluation
Annotation Workflows
Internal Benchmarks
AI Guardrails
Hallucination Detection
Responsible AI
Grounded/Citation-based Generation
Fine-tuning
Model Adaptation
AI Copilots
AI Assistants
Workflow Automation
Open-source contributions
Technical Blogging
Conference Speaking
AI/ML Community Involvement

Location

New York City

Work Type

Full-time
On-site

Experience Level

6+ years of experience

Benefits

Country specific benefits

About the Company

LexisNexis Legal & Professional is a global leader in legal information and analytics, serving customers in more than 150 countries.
We are investing aggressively in generative AI, agentic systems, and AI-native workflows that help legal professionals research faster, draft with confidence, and make better decisions in complex legal environments.
Our mission is to build trustworthy enterprise-grade AI systems that combine cutting-edge innovation with the accuracy, transparency, and reliability required in the legal industry.

Equal Opportunity

LexisNexis is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.