Summer Intern- Analytics Developer

Langley Federal Credit Union•Newport News, VA

1d•Onsite

About The Position

Langley Federal Credit Union has proudly served members since 1936, offering financial solutions to help individuals and families reach their goals. This summer, they are continuing their 10-week Summer Internship Program, providing an opportunity for motivated college juniors, seniors, and graduates to gain hands-on experience in the financial industry. Interns will work full-time (40 hours/week) on-site in Newport News, VA, from Monday June 1 to Friday August 7. The program focuses on meaningful projects, collaboration with experienced professionals, and a learning-rich environment, emphasizing inclusion, respect, and belonging. Interns will gain real-world experience, develop skills, and build a network in a supportive, team-oriented setting, with provided networking and professional development opportunities. The internship specifically involves providing analytical and predictive modeling support for Langley Federal Credit Union’s Success Sharing initiative by delivering one production-ready predictive model aligned to a single organizational goal. This role supports data-driven decision-making through data analysis, development of actionable insights, and clear communication of results, contributing to organizational goals by enabling targeted outreach through applied data science.

Requirements

Enrollment in or recent completion of a bachelor’s degree in data science, analytics, computer science, statistics, or a related field required, or an equivalent combination of education and experience.
Using Python for data analysis or modeling required.
Writing SQL queries against relational databases required.

Nice To Haves

Experience processing large amounts of structured and unstructured data preferred.
Exposure to statistical modeling or machine learning concepts preferred.
Familiarity with Python libraries such as pandas and scikit-learn preferred.

Responsibilities

Develops one predictive model in Python that estimates members likelihood to an organizational goal using data warehouse inputs.
Extracts, prepares, and analyzes member behavioral data using SQL and Python to support modeling objectives.
Performs exploratory data analysis to uncover key drivers of member behavior and guide effective feature engineering.
Engineers features and evaluates a limited set of candidate models, selecting a single final model based on defined performance metrics.
Validates model performance using a fixed train/test split and produces documented evaluation results.
Creates reproducible scripts or notebooks and a documented batch scoring process for the member population.
Documents model purpose, data sources, assumptions, limitations, risks, and recommended next steps.
Prepares and delivers a final presentation communicating predictive insights and business recommendations to technical and non-technical stakeholders.