Part-Time Applied Data Scientist

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About the role

Part-Time Applied Data Scientist This role is located at the Capital One Cambridge office, and you must be enrolled as a student at MIT to apply. As a part-time associate at Capital One, you’ll be part of a high-performing team that’s embracing the latest in computing cloud technologies to unlock opportunities that help everyday people save money and improve their financial lives. You'll help our customers solve their financial services challenge using groundbreaking techniques. You'll make valuable contributions from day one by continuously learning, engaging in diverse sets of experiences, and building close-knit relationships across the company. Role / team focus areas could include participating in analytical, risk management, machine learning, deep learning, GenAI initiatives across the enterprise. We'd love to find someone who is… • Intellectually curious. You ask why, you explore, and you are excited to imagine and create new ideas by inventing self-adaptive analytical, predictive, generative power or by tapping into unstructured data sources. You love turning data for insights into behaviors, intent and sentiment. • A builder. You are passionate about delivering new and better products, experiences, and capabilities to our customers and have a deep sense of ownership for your craft. • An innovator. You love trying new things, new combinations of tools, techniques, and engineering approaches even if you sometimes fail. • A Communicator. You seek to connect with teammates and business partners to share technical ideas that resonate with audience needs across verbal and written channels. Key Role Details • Participation in the part-time work arrangement requires that you are located in the continental United States • Designed to flex seamlessly around your coursework with a 10- to 20-hour weekly commitment, this role allows you to support our core data teams by supplementing existing projects to drive real business outcomes. On any given day, you’ll be: • Data Cleaning & Processing: Querying, cleaning, and preprocessing large structured and unstructured datasets to ensure data quality and readiness for downstream analysis. • Feature Engineering: Writing, testing, and validating code to extract compelling analytical features from raw bureau records and transactional data streams. • Executing ML Pipelines: Running and monitoring existing machine learning workflows and tooling to generate routine and/or ad hoc model diagnostics and track system performance. • Insights Generation: Aggregating data findings from internal and external sources to support the baseline summaries that inform credit policy and pilot programs. • Insights Investigation: Performing qualitative and quantitative data investigations and sensitivity analyses to identify trends, anomalies, and potential data risks. • Pod Collaboration: Supporting the day-to-day tactical data, analytical, and modeling needs of a cross-functional team alongside senior Data Scientists and Business Analysts. Basic Qualifications: • Currently pursuing a Bachelor’s degree in a quantitative field (mathematics, science, statistics, engineering) • At least 6 months of experience or academic work using open source programming languages • At least 6 months of experience or academic work using machine learning, AI applications (Claude Code, Codex, Gemini AI Suite) • Current student at Massachusetts Institute of Technology. Preferred Qualification: • Students with scholarships, in honors programs, or with departmental distinctions • 1+ year experience with Python and GenAI tools • 1+ year experience or academic work in statistics, mathematics, computer science, machine learning, GenAI, and data science • 1+ year  Experience or academic work with large scale relati

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