Computer Science Intern Jobs in Miami, FL (NOW HIRING)

Your projects will be drawn from our active data and AI roadmap and matched to your skills. Expect to spend the majority of your time on data and analytics work, with meaningful exposure to AI engineering on top of that foundation:

• Operational data analytics — Work hands-on with our operational data (work orders, labor, schedules, financials) to answer real business questions. Build queries, transformations, and analyses that go directly to operations leaders and finance partners.
• Dashboards and reporting — Design and build dashboards and recurring reports in Power BI. Translate stakeholder requirements into clear, well-modeled, performant visualizations.
• Data modeling and pipelines — Help shape how we structure operational and financial data for analytics. Contribute to data models, transformations, and lightweight pipelines that feed reporting and AI use cases.
• AI-augmented analytics — Build LLM-driven workflows that accelerate analytics work: automated data summaries, natural-language querying of operational data, document understanding (contracts, specs, inspection reports), and assistants that help internal teams self-serve information.
• Agentic workflows and AI tooling — Learn alongside the team how to use modern AI engineering tools (Claude Code, Codex, MCP-based integrations) to ship analytical work and prototypes faster and more reliably.
• Deployment and integration — Help take analytical prototypes from concept to a deployed internal service or scheduled job: basic CI/CD, APIs, and integration with existing platforms.
• Data governance and security — Learn how analytics and AI get built responsibly inside a corporate environment: secure handling of operational and financial data, access controls, and alignment with our security and compliance requirements.

• Direct mentorship from the AI Solutions Architect, with real ownership of analytical work that touches the business.
• Exposure to how data and AI are adopted inside a large, established services company — not a hypothetical exercise, but a real transformation in motion.
• Hands-on training in modern analytics and AI engineering practices: SQL, Python, dashboarding, agentic coding, evaluation, and production deployment.
• Competitive paid internship.
• Strong consideration for a full-time analyst, data engineering, or AI engineering role for high-performing interns.

You are a current undergraduate or graduate student pursuing one of the following (or a closely related field):

• Data Analytics
• Data Science / Statistics
• Computer Science / Software Engineering
• Cybersecurity
• Business Analytics / Management Information Systems

You should bring most of the following:

• Part-time availability — Availability to work approximately 25 hours per week.
• Strong SQL — Comfortable writing complex queries, joins, aggregations, window functions, and CTEs. Able to reason about query performance on large tables.
• Solid Python — Specifically for data work: pandas, basic data wrangling, and reading/writing common data formats.
• Analytical mindset — You can take an ambiguous business question, break it down, and figure out what data and what analysis would actually answer it.
• Dashboarding exposure — Coursework, internships, or personal projects using Power BI, Tableau, Looker, or a similar tool.
• Curiosity about LLMs and AI tools — You have built something with the OpenAI or Anthropic API, experimented with retrieval-augmented generation, written prompts that do useful work, or tried an agent framework. You do not need to have trained a model from scratch.
• Git fundamentals — Branching, pull requests, and working in a real shared codebase.
• Clear written communication — You can explain your analysis to people who are not engineers or data scientists.
• Professional maturity — You will be working with operational and financial data from a real business. Discretion, reliability, and attention to detail matter.

• Exposure to data warehouses or analytical databases (Snowflake, BigQuery, Redshift, Synapse, or SQL Server).
• Experience with dbt, Airflow, or similar transformation and orchestration tools.
• Familiarity with statistical methods: hypothesis testing, regression, basic forecasting.
• Exposure to LangChain, LlamaIndex, or similar AI orchestration frameworks.
• Coursework or projects in cybersecurity, identity, or cloud security.
• Background in facilities, services, or any operational/industrial context.