Manager Data Science - Global Payment Network AI Foundations

Manager Data Science - Global Payment Network AI Foundations

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 Global Network Analytics (GNA) team—specifically within our Foundational AI & Automation pillar—builds machine learning engines and AI agents to drive foundational, scalable intelligence across the payments ecosystem. We create core models and AI tools to power merchant intelligence, acceptance growth, fraud/risk decisioning, and enterprise automation.

In this role, you will:

• Partner with a cross-functional team of data scientists, software engineers, ML engineers, and product managers to deliver AI-powered capabilities that evaluate billions of network transactions and transform merchant intelligence.
• Leverage a modern tech stack—PyTorch, AWS, VectorDBs, and LLMs—to extract actionable insights from complex numerical, tabular, and textual data feeds.
• Serve as the domain expert in Natural Language Processing (NLP) and Large Language Models (LLMs), adapting, fine-tuning, and working with technology to deploy them for business users.
• Translate technical ML complexity into tangible business value and strategic goals.

The Ideal Candidate is:

• Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
• Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
• Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
• A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
• Technical. You’re comfortable with advanced ML and DL technologies including language models and are passionate about developing further. You have hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
• Influential. You are passionate about AI/ML and can bring along a cross functional team in breakthrough innovations. You communicate clearly and effectively to share your findings with non-technical audiences.
• You are experienced in training language models as well as have expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
• You have an engineering mindset as shown by a track record of delivering models at scale both in training data and inference volumes. You have experience in delivering libraries, platforms, or solution level code to existing products.

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, Mathematics, Computer Science, or a related quantitative field)  or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
  • 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 analytic…