Manager, Data Scientist - Recommendation & Personalization Systems

Capital One•San Jose, CA

About The Position

Data is at the center of everything we do. As a startup, Capital One disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988. This innovation and passion for data has led Capital One to become a Fortune 200 company and a leader in data-driven decision-making. As a Data Scientist, you will be part of a team leading the next wave of disruption using the latest in computing and machine learning technologies across billions of customer records to help people save money, time, and agony in their financial lives. This role is within an elite Applied AI team in AI Foundations, working at the intersection of deep research and real-world impact. The team is pioneering the next generation of personalized customer experiences across Capital One's web and mobile applications, leveraging high-scale ML models. The core mission involves architecting and deploying cutting-edge personalized recommendation engines, powered by original research into homegrown Foundation Models, advanced Reinforcement Learning techniques, and a state-of-the-art scalable architecture built for billions of interactions. The research agenda focuses on areas such as Causal Inference, Transformer-based architectures, and sophisticated Recommender Systems.

Requirements

Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics OR A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics OR A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics
At least 1 year of experience leveraging open source programming languages for large scale data analysis
At least 1 year of experience working with machine learning
At least 1 year of experience utilizing relational databases

Nice To Haves

PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
3+ years of hands-on experience building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices, including AWS, Kubeflow, and CI/CD pipelines
Deep expertise (4+ years) in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization
Extensive experience (4+ years) with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark

Responsibilities

Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Flex your interpersonal skills to translate the complexity of your work into tangible business goals