Data Analytics Intern

Trinity Industry is looking for Data Analytics Interns for our office in Dallas, TX . This position will focus on building, deploying, and scaling agentic AI applications and automated workflows across the enterprise. You will contribute to producing AI-first solutions using large language models, agent frameworks, and AI-assisted coding to deliver deployable apps, agents, automated workflows, and AI orchestration.

This role will provide valuable experience working with state-of-the-art enterprise platforms (Databricks and Palantir Foundry) and AI tools (pro licenses for Codex and Claude Code). The role offers hands-on mentorship from senior analytics professionals, exposure to production data and governance practices, and high visibility to business owners giving you real ownership of high-impact projects.

What You'll Do

Design, prototype, and deliver agentic systems that plan and execute multi-step business tasks. Build automated workflows and connectors that integrate internal systems and APIs. Implement LLM-based agents (prompting, tool-calling, memory, monitoring) and harden prototypes for handoff. Produce code and deployable artifacts using AI-assisted generation.

Collaborate with data analysts, data scientists, and data engineers as well as stakeholders to gain in-dept knowledge of business problems and likely solutions. Demo results and deliver concise handoff docs, runbooks, and impact metrics. What You'll Have: Master’s or PhD candidate or recent graduate in Data Science, Machine Learning, Computer Science, Applied Math, Statistics, Operations Research, or similar quantitative field.

Demonstrable experience building AI prototypes or agentic projects (coursework, research, personal projects, or employment). Generate, test, and iterate code (examples: Python, JavaScript, YAML) using an IDE (VS Code, Jet. Brains, etc.) with AI-assisted coding (GitHub Copilot, OpenAI Codex, Claude Code, Cursor, or similar).

Manual coding fluency is helpful but not mandatory. Familiarity with SQL and cloud data platforms (Databricks, Azure, AWS, or equivalent). Strong problem solving and communication skills; ability to present technical work to nontechnical stakeholders. Preferred / Nice-to-Have Practical experience with model deployment and monitoring basics (packaging/deploying prototypes, logging/observability, basic cost and drift checks).

Experience building or working with data/workflow pipelines or orchestration tools or familiarity with the concepts of scheduling and task orchestration. Experience working with unstructured text and retrieval approaches (RAG or index + LLM patterns) for document/question answering. Comfortable integrating services via REST APIs and using secure authentication patterns.