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About the Role

As a Data Scientist on the Card Payment Fraud Prevention team, you'll lead the charge against first-party fraud. You will build and deploy mission-critical machine learning models that operate across billions of transactions to secure the entire credit card portfolio. Your work will directly translate to massive financial protection and business value from reduced credit losses. The mission includes optimizing models for highly challenging and expanding segments to improve fraud capture rates and enhance customer safety.

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, 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 interpersonal skills to translate the complexity of your work into tangible business goals

Requirements

Expertise in challenging conventional thinking and working with stakeholders to identify and improve the status quo.
Ability to 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.
Comfort with open-source languages and a passion for developing further.
Hands-on experience developing data science solutions using open-source tools, cloud computing platforms, Objected-Oriented Programming (OOP) principles, and testing frameworks.
Experience building, validating, and backtesting models.
Knowledge of how to interpret a confusion matrix or a ROC curve.
Experience with clustering, classification, time series, and common ML modeling methodologies, particularly black box models like GBMs.
Skills to retrieve, combine, and analyze data from a variety of sources and structures.
Currently have, or be 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 plus 5 years of experience performing data analytics, A Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics, or A PhD in a quantitative field.
Master’s Degree in “STEM” field plus 3 years of experience in data analytics, or PhD in “STEM” field.
At least 1 year of experience working with AWS
At least 3 years’ experience in Python, Scala, or R
At least 3 years’ experience with machine learning
At least 3 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

Skills

Python
Conda
AWS
H2O
Spark
Ray
Kubernetes
SQL
Machine Learning
Data Analytics
Statistics
Economics
Operations Research
Analytics
Mathematics
Computer Science
MBA
STEM
Big Data
Distributed Computing
Model Risk Governance
GenAI

Location

Chicago, IL
McLean, VA
New York, NY

Work Type

Full-time

Experience Level

Principal Associate
5 years of experience performing data analytics
3 years of experience performing data analytics
1 year of experience working with AWS
3 years’ experience in Python, Scala, or R
3 years’ experience with machine learning
3 years’ experience with SQL

Education Level

Bachelor's Degree in a quantitative field
Master's Degree in a quantitative field
MBA with a quantitative concentration
PhD in a quantitative field
Master’s Degree in STEM field
PhD in STEM field

Salary/Compensations

Chicago, IL: $147,100 - $167,900
McLean, VA: $161,800 - $184,600
New York, NY: $176,500 - $201,400

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.

About the Company

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.
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.

Equal Opportunity

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.