Data Science Summer Internship

We are seeking a Data Science Intern to support the design, development, and evaluation of predictive models that inform business decisions across insurance / financial services use cases (e.g., risk, pricing, propensity, claims). The intern will work alongside experienced data scientists and contribute to real analyses using production-like, real-world datasets—not toy problems—while building strong foundations in applied machine learning, experimentation, and stakeholder communication.

What You’ll Do (Key Responsibilities)

Under the guidance, you will:

• Support predictive model development for use cases such as risk, pricing, propensity, and claims.
• Perform data exploration and feature engineering using Python and SQL.
• Clean and prepare datasets from multiple sources (e.g., EDP, PRS, finance/claims systems).
• Run experiments and model evaluations, including train/validation splits, performance metrics, and back-testing.
• Translate results into clear summaries and visuals for business stakeholders.
• Contribute to documentation of datasets, models, and experiments within internal repositories.

Required Skills & Competencies

• Python (Pandas, NumPy; plus scikit-learn or similar ML libraries).
• Solid understanding of statistics and probability (distributions, confidence intervals, hypothesis testing).
• Working knowledge of machine learning concepts (classification, regression, model evaluation).
• SQL: ability to query, join, and aggregate data from relational sources.
• Experience with Jupyter / VS Code and version control (Git) is a plus.

Core Competencies

• Strong analytical/problem-solving skills and curiosity about “why the data looks this way.”
• Ability to explain technical ideas in clear, simple language to non-technical audiences.
• Attention to detail and comfort working with messy, real-world data.
• Reliability, ownership, and willingness to ask questions early.

What You’ll Learn / Professional Development

During the internship, you can expect:

• Work on real business problems with measurable outcomes (e.g., underwriting, claims, finance).
• Close mentorship from experienced data scientists on coding practices, model design, and experiment setup.
• Participation in team rituals such as stand-ups, code reviews, and design discussions, learning how data science operates in an enterprise environment.
• Opportunities to present findings to business stakeholders, strengthening communication and storytelling skills.

What You’ll Gain by the End of the Internship

By the end of the internship, the Data Science Intern can expect to have:

• Hands-on experience building, evaluating, and helping deploy at least one end-to-end ML or advanced analytics use case.
• Stronger skills in Python, SQL, and statistics applied to production-like datasets.
• Exposure to industry tooling and practices (e.g., data platforms, BI tools, Git, issue tracking).
• A concrete portfolio artifact (or internal case study) to speak to in future interviews.
• Practical understanding of how data science supports insurance / financial services decisions.

Minimum Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Coursework or project experience in machine learning/statistics and data analysis.

Nice to Have

• Experience with Git-based workflows (pull requests, code reviews).
• Familiarity with model monitoring concepts or production analytics practices.
• Exposure to insurance, claims, underwriting, pricing, or financial datasets (helpful but not required).