Senior Data Scientist II

Job Description : Senior Data Scientist IISenior Data Scientist IIJob DetailsLocation: New York, NY (On-site, Remote options available)Company: LexisNexis Legal & Professional (RELX)Job Type: Full-timeApplication Deadline: 05/29/2026Eligible for Bonus: Yes (Annual incentive bonus)Base Salary Range (USD)General: $104,900 - $174,700Illinois (State): $110,100 - $183,500Chicago, IL: $115,400 - $192,200Maryland: $110,100 - $183,500Ohio: $99,700 - $166,000About The RoleLexisNexis Legal & Professional is seeking a Senior Data Scientist II to lead the design and validation of Agentic AI-driven product capabilities within the legal domain. This role leverages machine learning, NLP, and Large Language Models (LLMs) to solve complex legal workflows, driving experimentation and translating validated approaches into scalable, customer-facing solutions.Key ResponsibilitiesDevelop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).Define agentic workflows and reasoning strategies for multi-step legal tasks.Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).Analyze large-scale legal datasets to extract insights and improve model performance.Establish best practices for model evaluation, validation, and benchmarking.Translate experimental results into clear product recommendations and business impact.Collaborate with product managers, legal experts, and engineers to align solutions with user needs.Mentor team members and provide technical leadership in data science and AI.Qualifications & RequirementsEducationMaster's degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).Technical SkillsStrong experience in machine learning, NLP, and LLM-based modeling.Proficiency in Python and data analysis tools.Experience with generative AI techniques (e.g., prompt engineering, RAG).Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.Hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.Strong experience designing and running experiments, including model evaluation and iteration.Strong foundation in statistics, modeling, and large-scale text processing.Soft SkillsTechnical leadership and mentoring capabilities.Ability to translate technical results into business recommendations.