Summer Associate Intern (Data Scientist)

We are seeking a highly motivated and detail-oriented Data Scientist Summer Associate to join the Member Insights & Experience Analytics team within Lending Strategy. This internship is designed for graduate-level students who are passionate about data science, machine learning, analytics, and improving customer experiences through data. The Member Insights & Experience Analytics team serves as the voice of the member by leveraging advanced analytics to better understand member behaviors, preferences, and experiences across the lending lifecycle. The team combines member data, digital interactions, and voice-of-the-member feedback to deliver actionable recommendations that improve member engagement, satisfaction, and business outcomes. This work supports Lending leadership, product teams, operational partners, and member experience stakeholders. As a Summer Associate, you will work alongside data scientists and analytics professionals to develop models, uncover insights, and translate member interaction data into meaningful business recommendations. A primary focus of the internship will be leveraging advanced analytics and AI techniques to analyze lending-related member calls, identify emerging themes, measure sentiment, and uncover opportunities to improve the member experience. The Summer Associate Program is a 12-week internship program beginning in May 2027 and ending in August 2027. Students will work on impactful projects and meaningful work during their internship. To qualify for this position, applicants must be currently pursuing a degree from an accredited college or university and have an anticipated graduation date of December 2027 or later. Current Data Science Projects 1. Lending Call Sentiment Analytics Objective : Measure and monitor member sentiment across lending-related interactions to identify opportunities to improve the borrower experience. Description: Develop sentiment models using call transcripts and interaction data to identify positive, neutral, and negative member experiences. Analyze sentiment trends across lending products, servicing processes, and member journeys to uncover pain points, operational challenges, and opportunities to improve satisfaction and engagement. Findings will support Voice of the Member initiatives and experience improvement efforts. 2. Lending Call Topic Modeling Objective: Identify and track key themes, concerns, and emerging issues within member lending conversations. Description: Apply Natural Language Processing (NLP) techniques to call transcripts and interaction data to automatically classify and cluster member conversations. The model will surface common topics such as application issues, underwriting questions, servicing inquiries, payment concerns, digital experience challenges, and member friction points. Insights will help business leaders prioritize improvements and address emerging member needs. 3. Voice of the Member Insights Dashboard Objective: Deliver self-service analytics that provide visibility into member experience trends and emerging issues. Description: Build interactive dashboards that combine sentiment scores, topic trends, call volumes, operational metrics, and member outcome data. These dashboards will help stakeholders monitor member experience KPIs, identify areas requiring attention, and evaluate the impact of process improvements. Responsibilities Collaborate with Lending Strategy, Member Experience, Product, and Operational partners to understand business challenges and translate them into analytical solutions. Perform data preparation, feature engineering, exploratory data analysis (EDA), and model development using structured and unstructured data sources. Develop Natural Language Processing (NLP) models to analyze lending-related call transcripts and member feedback. Build sentiment analysis and topic modeling solutions to identify member concerns, emerging trends, and experience improvement opportunities. Create dashboards and visualizations using Power BI to communicate findings to business stakeholders. Write efficient and scalable code in Python and SQL to manipulate and analyze large datasets. Apply statistical modeling, machine learning, and AI techniques to solve business problems and uncover actionable insights. Present findings and recommendations to technical and non-technical audiences. Document methodologies, model assumptions, and analytical processes to support governance and knowledge sharing. Support the development of data products and AI-enabled analytics capabilities within Lending Strategy. Perform other related duties as assigned Must be currently enrolled in, or planning to enroll in, a graduate-level program (Master’s) in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a closely related field following completion of undergraduate studies. Proficiency in Python and SQL. Familiarity with machine learning and Natural Language Processing (NLP) techniques. Experience with data cleaning and exploratory data analysis (EDA). Familiarity with data visualization tools such as Power BI and Dash. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Desired Qualifications Demonstrate intellectual curiosity by asking thoughtful questions, challenging assumptions, and independently identifying opportunities for deeper analysis that improve understanding of member behavior and experience. Experience with R programming. Background in statistics, mathematics, or machine learning. Exposure to big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., Azure, AWS, GCP). Understanding of version control systems like Git. What You’ll Gain Hands-on experience solving real business problems using data science. Mentorship from experienced professionals in analytics and business intelligence. Exposure to enterprise-level tools and technologies. Opportunity to present your work to senior leadership. A collaborative and inclusive work environment. Additional Information Hours: Monday – Friday, 8:00AM - 4:30 PM ET Location: 820 Follin Lane, Vienna, VA 22180