Data Science Intern

Berkley • Houston, TX

20h

About The Position

We are seeking a highly motivated Data Science Intern who is interested in exploring data, experimenting with modeling approaches, and developing analytical solutions that influence business decisions. You will support the Advanced Analytics team by performing exploratory data analysis (EDA), data engineering, feature engineering, and statistical modeling to identify, prioritize, and explore predictive features for insurance loss modeling. The role will involve collaboration with stakeholders from the underwriting, pricing, and operations departments to ensure that data products align with business needs.

Requirements

• Previous quantitative research experience (statistics, applied mathematics, computer science, or related field) in a course, personal project, or previous role
• Experience using databases and data modeling concepts, with proficiency in SQL (cloud data platforms a plus)
• Python proficiency strongly preferred (pandas, NumPy, scikit-learn; notebooks)
• Exploratory data analysis skills (profiling, missingness, outlier detection) and feature engineering experience

Nice To Haves

• Exposure to loss modeling, pricing analytics, or risk scoring is a plus
• Oil & Gas or Insurance domain knowledge is a plus (e.g., geospatial data, underwriting context)
• Familiarity with Agile/SDLC practices is a plus
• Current master’s student or recent graduate in Data Science, Analytics, Statistics, Engineering, Computer Science, or a related field is a plus

Responsibilities

• Assess the business value of a third-party data source using an established scoring rubric
• Learn core insurance concepts and build foundational knowledge of Property & Casualty insurance products, markets, and underwriting processes
• Conduct exploratory data analysis (EDA) on insurance and oil & gas datasets to understand patterns, data quality/coverage, and modeling opportunities
• Explore data engineering approaches and test methods for entity matching, including geospatial and temporal linkage techniques
• Engineer features by creating, validating, and maintaining derived variables that support analytical and predictive modeling
• Collaborate with business and technology partners to clarify requirements, understand key variables, and align on acceptance criteria
• Prototype predictive models and compare lightweight models against existing benchmarks
• Quantify value by estimating cost/benefit and return on investment for proposed features, models, and data products
• Develop analytics workflows and implement repeatable processes for EDA, feature creation, and model evaluation
• Plan model integration by working with engineering teams to outline how analytical models will be incorporated into production systems and reporting tools
• Create analytical datasets and dashboards to prepare datasets and build reports that help stakeholders make informed decisions
• Ensure data and model quality by conducting QA checks on datasets, metrics, and prototype models
• Maintain documentation for datasets, features, models, and analytical processes
• Automate repetitive tasks using scripting and AI tools to streamline work and improve productivity

What This Job Offers

• Job Type: Part-time
• Career Level: Intern