Data Science Intern

## About Our Team LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX, 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. ## About Our Internship Program Our internships provide hands-on experience through practical product and data science work, weekly development sessions, and exposure to data scientists, engineers, product managers, UX, and customer-focused teams. Interns will learn modern AI-enabled development practices, including data preparation, experimentation, model evaluation, responsible AI, product telemetry, and evidence-based iteration. ## Program Details - Start Date: June 7, 2027 - Duration: 11-12 weeks - Location: On-site in Raleigh, NC. - Relocation assistance: NOT provided. - Graduation Requirements: Applicants must have a graduation date AFTER June 2027 to be eligible for consideration. ## About the Role As a Data Science Intern, you will analyze complex data sets, develop practical data mining and statistical analysis skills, and contribute to machine learning and AI-enabled product solutions. You will work closely with data scientists and engineers to explore model performance, data quality, retrieval quality, AI output quality, and customer-impact metrics for products that use trusted legal and professional content. You will help evaluate and improve AI-powered experiences while building valuable hands-on skills in data science, machine learning, and responsible AI practices. ## Responsibilities - Contribute to collecting, cleaning, organizing, and documenting datasets for analysis, model development, and evaluation. - Collaborate with data science, engineering, product, and UX team members to develop, test, and improve predictive models, machine learning workflows, and AI-enabled product capabilities. - Conduct exploratory data analysis to identify trends, patterns, data-quality issues, and opportunities to improve customer outcomes. - Support experiments involving generative AI, large language models, embeddings, retrieval-augmented generation, prompt patterns, and agentic workflows. - Contribute to model and AI-output evaluation, including metrics for accuracy, grounding, relevance, reliability, bias, safety, and customer usefulness. - Create visualizations, reports, dashboards, or notebooks that communicate insights, experiment results, and model performance clearly. - Document assumptions, limitations, evaluation methods, and responsible AI considerations so work can be reviewed, reproduced, and reused by global teams. ## Requirements - Be a currently enrolled student pursuing a degree in data science, computer science, statistics, mathematics, engineering, or a related field of study. - Have experience using programming languages and tools such as Python, R, SQL, notebooks, and common data science libraries. - Demonstrate a foundation in statistics, machine learning, data visualization, and analytical problem-solving. - Demonstrate AI fluency or curiosity, including awareness of generative AI, large language models, embeddings, RAG, model evaluation, model limitations, and responsible AI practices. - Demonstrate effective problem-solving, critical-thinking, verbal, and written communication skills. - Be detail-oriented and able to work both independently and collaboratively with others. - Demonstrate an interest in learning, experimentation, data-driven decision-making, and staying current with AI and data science trends.