Senior Manager, Data Science — Card Fraud Prevention

Capital One; McLean (VA); On-site; USD 230,000 - 262,000; Full time

Job summary

Capital One is seeking a Senior Manager, Data Scientist for Card Fraud Prevention in McLean, VA. You will lead a data science team building ML models to detect and mitigate fraud across massive data sets, using Python, Spark, AWS, and modern ML tooling.

You will partner with product and engineering teams, translate insights into business actions, and drive impact while mentoring team members. Strong leadership and technical skills are required.

Qualifications

• Bachelor's in a quantitative field with 7+ years analytics experience.
• Master's in a quantitative field or MBA with quantitative focus plus 5 years analytics.
• PhD in a quantitative field plus 2 years analytics experience.
• At least 2 years using open source languages for large-scale data analysis.
• At least 2 years of experience in machine learning.
• At least 2 years of experience with relational databases.

Responsibilities

• Partner with cross-functional data scientists, software engineers, and product managers to deliver a product customers love.
• Use Python, Conda, AWS, Spark, and related tools to reveal insights in large numeric and textual data.
• Build machine learning models through design, training, evaluation, and implementation.
• Translate complex work into tangible business goals.

Skills

Open source languages

Machine learning

Relational databases

Leadership

Communication

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA with quantitative concentration

PhD in quantitative field

Tools

Python

Scala

R

AWS

Spark

Kubernetes

PyTorch

H2O

Conda

Job description

Capital One is seeking a Senior Manager, Data Scientist for Card Fraud Prevention in McLean, VA. You will lead a data science team building ML models to detect and mitigate fraud across massive data sets, using Python, Spark, AWS, and modern ML tooling.

You will partner with product and engineering teams, translate insights into business actions, and drive impact while mentoring team members. Strong leadership and technical skills are required.