Data Science Intern

QBE is an insurance company focused on using machine learning and artificial intelligence to transform the insurance industry. The Data Science Intern will build and evaluate models, develop deployment pipelines, write Python code, and collaborate on solutions that improve underwriting and operations.

What you'll do:

• Use data to uncover insights that improve underwriting and operations
• Build and evaluate models, explore new features, and collaborate with teams to deliver measurable impact
• Contribute to the development and maintenance of deployment pipelines, ensuring models are robust, reproducible, and scalable
• Write clean, efficient, and well-documented Python code using libraries such as pandas, numpy, pydantic, and FastAPI
• Learn and apply modern ML tools and frameworks such as MLflow and agentic programming
• Stay curious - ask questions, seek feedback, and continuously improve your skills

What they're looking for:

• Current enrollment in a Bachelor's in Computer Science, Engineering, Mathematics, Data Science, or a related field
• Working knowledge of Python; ability to quickly learn new Python packages and programming concepts
• This role is hybrid, where you will be expected to be in the office 4 days per week at our Madison, WI office
• Applicants must be authorized to work in the United States on a full-time basis without the need for current or future sponsorship
• Proficiency in Python and familiarity with core data science libraries (e.g., pandas, numpy, scikit-learn)
• Exposure to cloud platforms (especially Azure) and containerization tools like Docker
• Understanding of MLOps concepts such as Git, CI/CD, and model lifecycle management
• Familiarity with model validation and monitoring practices
• Strong communication skills and a collaborative mindset
• Eagerness to learn and grow - technically and professionally

Benefits:

• This role is hybrid, where you will be expected to be in the office 4 days per week at our Madison, WI office
• Learn from experienced peers
• Grow in a fast-paced, supportive environment