Manager, Data Scientist - Card Payment Fraud Prevention

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. Team Description The Card Payment Fraud Prevention data science team detects and mitigates first‑party fraud by building and deploying machine learning models that keep customer accounts safe and compliant. Leveraging big data and a modern tech stack—including Python, Spark, Ray, H2O, PyTorch, and Kubernetes—the team delivers production‑ready insights with a focus on both speed and sustainable impact, combining deep experience in traditional ML with an appetite for AI‑based development. 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 through training, evaluation, validation, and implementation, monitoring, and supporting continuous model deployment and maintenance in a production environment Collaborate on the design and maintenance of production data science solutions, including writing clear technical documentation and ensuring models adhere to software development best practices Manage model risk and maintain regulatory compliance across the model lifecycle, which includes maintaining model inventory records, executing model testing and change control protocols, and collaborating on independent model validation and compliance risk assessments Flex your interpersonal skills to translate the complexity of your work into tangible business goals The Ideal Candidate is: An expert. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You rapidly come up to speed to pair technical skills with subject‑matter expertise in your domain, conveying knowledge and shaping next steps for both you and the team you work in Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools, cloud computing platforms, object‑oriented programming principles, and testing frameworks Statistically‑minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, time series, and common ML modeling methodologies, particularly black‑box models like GBMs A data guru. “Big data” doesn’t faze you. 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, 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 a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics At least 1 year of experience working with AWS At least 4 years’ experience in Python for large‑scale data analysis At least 4 years’ experience with machine learning specifically developing models that have gone into production At least 4 years’ experience with SQL Demonstrated experience with big data and distributed computing, using Spark or another comparable framework Demonstrated experience with model risk governance Demonstrated experience technically leading and developing a team Demonstrated experience with both traditional machine learning and emerging GenAI techniques, with primary focus on traditional ML model development, not GenAI‑only experience Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. Salary and Compensation Chicago, IL: $179,400 - $204,700 for Manager, Data Science McLean, VA: $197,300 - $225,100 for Manager, Data Science New York, NY: $215,200 - $245,600 for Manager, Data Science Other locations: salary range associated with the location; actual annualized salary will be reflected in the offer letter This role is eligible to earn performance‑based incentive compensation, which may include cash bonus(es) and/or long‑term incentives (LTI) Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level. Equal Opportunity Employer (EOE) Capital One is an equal‑opportunity employer (EOE, including disability/veteran) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Compliance with Criminal Background Inquiries Capital One will consider 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. #J-18808-Ljbffr Capital One