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Manager Data Science - Global Payment Network AI Foundations

Capital One · Riverwoods, IL · Full time

Posted Oct 5, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a time you led a project with data scientists, engineers and product managers. How did you keep it moving?

What they are checking: Whether they can lead cross-functional work and handle trade-offs between teams.

How do you explain a complex model's results to business leaders who are not technical?

What they are checking: Whether they can make results clear and useful for decisions.

How do you develop and coach data scientists on your team?

What they are checking: Whether they invest in growing people as a manager, not just in their own work.

What would you do

A model that worked well last year is now performing worse. How do you investigate?

What they are checking: Whether they check for data drift, pipeline changes and shifts in behavior in a structured way.

About the work

How would you build a model to improve merchant acceptance across a payment network? What data would you want?

What they are checking: Whether they can frame a real business problem as a modeling problem with the right data and target.

What is different about building fraud models compared to other classification models?

What they are checking: Whether they understand class imbalance, changing fraud patterns and the cost of false positives and negatives.

How have you built AI agents or LLM tools to automate work, and how did you make sure they were reliable?

What they are checking: Whether they have hands-on experience with AI agents and think about evaluation and guardrails.

How do you decide whether a model is ready to go into production?

What they are checking: Whether they consider validation, monitoring, fairness and business impact before shipping.

How would you build a foundational model or feature set that many teams could reuse?

What they are checking: Whether they think about scalable, shared AI foundations instead of one-off models.

How do you handle model risk and governance in a regulated financial company?

What they are checking: Whether they understand documentation, validation and review expectations for models at a bank.

Practice this interview out loud.

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The full job description

As published by Capital One.

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 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 Preferred Qualifications: • PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in model development • At least 4 years’ experience in Python, Scala, or R • At least 4 years’ experience with machine learning • At least 4 years’ experience with SQL • Experience with Large Language Models (LLMs), including utilizing embedding models and working with vector databases • Experience designing and implementing AI workflows and autonomous AI agents Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Richmond, VA: $179,400 - $204,700 for Mgr, Data Science New York, NY: $215,200 - $245,600 for Mgr, Data Science Riverwoods, IL: $179,400 - $204,700 for Mgr, Data Science Chicago, IL: $179,400 - $204,700 for Mgr, Data Science Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

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