Manager, Data Science - AI Software Engineering
AI summary of the role
Lead a data science team within AI Foundations to design and deliver scalable AI architectures for software development, including multi-agent solutions for code generation, migration, and troubleshooting.
What you’ll do
- 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 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
What you’ll bring
- Bachelor's in quantitative field plus 6 years data analytics, or Master's plus 4 years, or PhD plus 1 year
- At least 1 year experience leveraging open source programming languages for large scale data analysis
- At least 1 year experience working with machine learning
- At least 1 year experience utilizing relational databases
Technologies
Python · AWS · LangGraph · MCP · RAG · LoRA · QLoRA · Spark · H2O · Conda
Source and classification
Internal deployment & tooling · Evidence for this classification:
team designs, builds, and delivers state-of-the-art, scalable AI architectures that transform how software is developed at Capital One. We partner closely with product and engineering teams to create multi-agent solutions across the software development lifecycle—including code generation, migration, troubleshooting, root-cause analysis, and documentation—leveraging technologies such as LangGraph, MCP, agent-to-agent protocols, and advanced model customization techniques. Role Description In this role, you will: 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
More from the job description
Manager, Data Science - AI Software Engineering Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Team Description The AI Foundations – AI Software Engineering Data Science team designs, builds, and delivers state-of-the-art, scalable AI architectures that transform how software is developed at Capital One. We partner closely with product and engineering teams to create multi-agent solutions across the software development lifecycle—including code generation, migration, troubleshooting, root-cause analysis, and documentation—leveraging technologies such as LangGraph, MCP, agent-to-agent protocols, and advanced model customization techniques. Role Description In this [... source excerpt omitted ...] You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. Basic Qualifications: 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 A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathemati [... source excerpt omitted ...] mming 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 Preferred Qualifications: PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) Experience working with AWS Experience building production-grade agentic platforms, including RAG and graph-augmented systems, MCP or tool-calling integrations Demonstrated expertise in advanced model customization techniques—such as fine-tuning, parameter-efficient tuning (LoRA/QLoRA), reinforcement l
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