Quest Analytics Internship Program - Summer 2027 in Kansas City

Internship Opportunities:

We're looking for talented and curious students to join our Summer 2027 Technology Internship Program across three areas:

Software Engineering – Contribute to the full software development lifecycle by helping plan, design, develop, test, and deploy features and applications that support our healthcare technology solutions. Work alongside experienced engineers to write quality code, troubleshoot issues, and improve existing products and functionality. Gain hands-on experience using modern AI-assisted development tools, including Claude Code, to support coding, debugging, testing, documentation, and development workflows.

Data Engineering – Help build, maintain, and improve the data infrastructure that powers our products and analytics solutions. Work with large, complex datasets to support daily data operations, develop and optimize data ingestion pipelines, automate data processes, and monitor and troubleshoot data workflows to ensure reliable, high-quality data. Explore opportunities to use AI-assisted tools, including Claude Code, to accelerate development, automate workflows, and improve engineering efficiency.

Data Science – Apply statistics, machine learning, and AI to explore complex healthcare data and solve real-world business problems. Support data exploration, model development and evaluation, experimentation, and the communication of analytical findings. Work with emerging technologies, including generative AI, Large Language Models (LLMs), and AI-assisted development tools such as Claude Code, to explore new approaches to analytics and problem solving.

Interns will be aligned to a team and project based on their skills, interests, academic background, and business needs.

AI & Emerging Technology

We're looking for students who are already experimenting with AI, not just learning about it in the classroom. Strong candidates will have hands-on experience using generative AI and AI-assisted development tools through coursework, personal projects, research, internships, hackathons, or other practical applications.

Experience may include tools and technologies such as Claude, Claude Code, LLMs, prompt engineering, AI-assisted coding, RAG, embeddings, vector search, AI agents, or other applied AI/ML technologies.

We're especially interested in students who can demonstrate how they've used AI to build something, solve a problem, automate a process, analyze data, or improve the way they work—and who are curious about continuing to experiment with emerging technology.

What You'll Need:

Software & Data Engineering Skills:

• Understanding of object-oriented programming concepts; C#/.NET experience is preferred
• Ability to troubleshoot and debug applications
• Ability to write performant SQL queries against complex data models
• Understanding of relational databases, data structures, and software development fundamentals
• Exposure to data pipelines, ETL/ELT processes, distributed data processing, APIs, or cloud-based technologies is a plus
• Familiarity with source control and collaborative development using Git

Data Science Skills:

For candidates interested in the Data Science track, we're also looking for exposure to or interest in:

• Statistics, probability, and exploratory data analysis
• Machine learning fundamentals, including supervised and unsupervised learning
• Classification and regression techniques
• Model development, validation, and evaluation
• Feature engineering and data preprocessing
• Natural language processing (NLP)
• Python data science libraries such as pandas, NumPy, scikit-learn, or similar tools
• Data visualization and communicating analytical findings
• Experimentation, hypothesis testing, and quantitative problem solving
• Generative AI and Large Language Models (LLMs) are a plus
• Exposure to prompt engineering, embeddings, vector search, retrieval-augmented generation (RAG), or other applied AI techniques is a plus

Soft Skills:

• Strong communication and collaboration skills
• Ability to communicate data topics and results clearly
• Self-motivated, proactive, and effective in a remote environment
• Strong problem-solving mindset and team player attitude