Data Science Internship

LexisNexis•Raleigh, NC

1d•Onsite

About The Position

This internship offers a valuable opportunity for current students to apply machine learning, generative AI, and data-driven insights to real-world products. Interns will learn from experienced professionals and contribute to innovative AI-enabled solutions. LexisNexis Legal & Professional, part of RELX, is a global provider of information-based analytics and decision tools. The company has a long history of deploying AI and advanced technologies in the legal market, focusing on ethical and powerful generative AI solutions. They employ over 2,000 technologists, data scientists, and experts who develop, test, and validate solutions in line with RELX Responsible AI Principles. The internship program provides hands-on experience through practical product and data science work, weekly development sessions, and exposure to various teams. Interns will learn modern AI-enabled development practices, including data preparation, experimentation, model evaluation, responsible AI, product telemetry, and evidence-based iteration.

Requirements

Currently enrolled student pursuing a degree in data science, computer science, statistics, mathematics, engineering, or a related field of study.
Experience using programming languages and tools such as Python, R, SQL, notebooks, and common data science libraries.
Foundation in statistics, machine learning, data visualization, and analytical problem-solving.
AI fluency or curiosity, including awareness of generative AI, large language models, embeddings, RAG, model evaluation, model limitations, and responsible AI practices.
Effective problem-solving, critical-thinking, verbal, and written communication skills.
Detail-oriented and able to work both independently and collaboratively with others.
Interest in learning, experimentation, data-driven decision-making, and staying current with AI and data science trends.

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.