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Internship Nfl Data Analytics Jobs in Texas (NOW HIRING)

Trinity Industries

Data Analytics Intern

Dallas, TX · On-site

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 ...

TrinityRail

Data Analytics Intern

Dallas, TX · On-site

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 ...

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Internship Nfl Data Analytics information

What are the key skills and qualifications needed to thrive as an NFL Data Analytics intern, and why are they important?

To thrive as an NFL Data Analytics Intern, you need a solid foundation in statistics, data analysis, and a relevant field of study such as mathematics, computer science, or sports analytics. Familiarity with programming languages like Python or R, experience with data visualization tools, and knowledge of databases such as SQL are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills set outstanding candidates apart. These skills are crucial for accurately interpreting complex football data, supporting team decision-making, and clearly conveying insights to coaches and executives.

What types of projects and responsibilities can I expect during an NFL Data Analytics internship?

As an NFL Data Analytics intern, you can expect to work on projects involving the collection, cleaning, and analysis of large sports datasets, such as player statistics, game outcomes, and tracking data. Typical responsibilities include building data visualizations, supporting predictive modeling efforts, and assisting in the preparation of reports for coaches and front-office staff. Collaboration is common, as you'll often work with data scientists, software developers, and football operations personnel. This hands-on experience provides valuable exposure to real-world sports analytics and the opportunity to contribute meaningful insights to decision-makers within the organization.

What is an NFL Data Analytics internship?

An NFL Data Analytics Internship is a temporary, hands-on position where interns assist the NFL or its affiliated teams in analyzing large sets of football data. Interns typically work with statistics, game footage, and player performance metrics to help provide actionable insights for coaching, scouting, or business decisions. The role often involves using data analysis tools and programming languages such as Python or R. It's an excellent opportunity for students or recent graduates interested in sports analytics to gain real-world experience and build valuable industry connections.

What is the difference between Internship Nfl Data Analytics vs Data Analyst Intern?

AspectInternship Nfl Data AnalyticsData Analyst Intern
Required CredentialsRelevant coursework, basic statistical knowledgeRelevant coursework, basic statistical knowledge
Work EnvironmentSports industry, NFL teams, sports analytics firmsVarious industries, corporate offices, tech companies
Employer & Industry UsagePrimarily sports teams and sports analytics companiesBroadly used across industries including finance, marketing, tech

Internship Nfl Data Analytics and Data Analyst Intern roles share similar educational requirements and skills. However, Internship Nfl Data Analytics is specifically focused on sports data within the NFL industry, while Data Analyst Intern roles are more diverse across various sectors. The work environment for NFL analytics internships is centered around sports organizations, whereas data analyst internships can be found in multiple industries.

Internship Nfl Data Analytics jobs near you

Data Analytics Intern

Trinity Logistics Group

Dallas, TX

Internship

Re-posted 16 days ago

Job description

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, JetBrains, 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.

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