Real-Time Debit Fraud Data Scientist

Capital One; McLean (VA); On-site; USD 162,000 - 185,000; Full time

Job summary

Capital One invites applications for a Principal Associate, Data Scientist in Customer Protection Debit Transaction Fraud Data Science in McLean, VA. The role focuses on building and deploying fraud detection models across billions of transactions using modern ML techniques and a broad tech stack.

Ideal candidates combine hands-on modeling with strong collaboration across data scientists, engineers, and product managers to advance real-time decision systems and protect customers from fraud.

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 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;
• A PhD in a quantitative field.

Responsibilities

• Partner with a cross-functional team to deliver a product that measurably keeps customers safe from fraudulent activities.
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, SQL — to reveal insights from large datasets.
• Build machine learning models through all phases of development: design, training, evaluation, validation, and deployment.
• Translate complex work into tangible business goals.

Skills

Python

SQL

Gradient boosting

XGBoost/LightGBM

Kubeflow Pipelines

Spark

Education

Bachelor's Degree in a quantitative field

Master's Degree in a quantitative field or MBA with quantitative concentration

PhD in a quantitative field

Tools

Conda

AWS

H2O

Snowflake

Snowpark

Kubeflow Pipelines

Kubernetes

Job description

Capital One invites applications for a Principal Associate, Data Scientist in Customer Protection Debit Transaction Fraud Data Science in McLean, VA. The role focuses on building and deploying fraud detection models across billions of transactions using modern ML techniques and a broad tech stack.

Ideal candidates combine hands-on modeling with strong collaboration across data scientists, engineers, and product managers to advance real-time decision systems and protect customers from fraud.