Principal Associate, Data Scientist - US Card (Emerging Fraud)

Join to apply for the Principal Associate, Data Scientist - US Card (Emerging Fraud) role at Capital One .

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by personallyizing every credit card offer using statistical modeling and relational databases. Fast-forward a few years, our passion for data has skyrocketed us to a Fortune 200 company and a leader in data‑driven decision making.

Team Description

The Emerging Fraud team seeks a hands‑on model developer to build and enhance next‑generation fraud detection models. In this role, you will develop key initiatives, focusing on transaction fraud, applying machine learning to solve complex, real‑world challenges and directly stop new fraud tactics.

Role Description

• 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.
• Translate the complexity of your work into tangible business goals through strong interpersonal skills.

The Ideal Candidate is

• Customer first—analyzing and creating solutions with a passion for doing the right thing for customers.
• Innovative—continually researching and evaluating emerging technologies, staying current on state‑of‑the‑art methods and applications.
• Creative—excelling at defining large, undefined problems, asking questions, and pushing to find answers.
• Technical—comfortable with open‑source languages and passionate about developing data science solutions on cloud platforms.
• Statistically‑minded—experienced building, validating, and backtesting models; interpreting confusion matrices, ROC curves, clustering, classification, sentiment analysis, time series, and deep learning.
• Data guru—proficient at retrieving, combining, and analyzing data from diverse sources and structures to unlock insights.

Basic Qualifications

• Currently has, or is in the process of obtaining one of the following with an expectation to obtain the required degree 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 5 years of data analytics experience.
• 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 3 years of data analytics experience.
• A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field).

Preferred Qualifications

• Master’s Degree in STEM (Science, Technology, Engineering, or Mathematics) plus 3 years of data analytics experience, or a PhD in STEM.
• 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.

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

Salary

• Chicago, IL: $144,200 - $164,600
• McLean, VA: $158,600 - $181,000
• New York, NY: $173,000 - $197,400
• Richmond, VA: $144,200 - $164,600

Senior Level

Mid‑Senior level

Employment Type

Full‑time

Job Function

Engineering and Information Technology

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 qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.

This role is expected to accept applications for a minimum of 5 business days.