Fraud Data Science Manager

Capital One; Richmond (VA); On-site; USD 176,000 - 201,000; Full time

Job summary

Capital One is seeking a Manager, Data Science – US Card (Fraud) to lead fraud modeling initiatives. You will partner with data scientists, software engineers, and product managers to deliver customer‑facing ML products that protect customers and assets.

You’ll work with Python, AWS, H2O, Spark, and other tools to build end‑to‑end models, validate them, and translate complex analytics into actionable business decisions in a high‑scale environment.

Qualifications

• Bachelor’s degree in a quantitative field plus 6 years of analytics experience
• Master’s degree in a quantitative field or MBA with quantitative concentration plus 4 years of analytics
• PhD in a quantitative field plus 1 year of analytics experience
• At least 1 year of experience leveraging open source programming languages for large scale data analysis
• At least 1 year of experience with machine learning
• At least 1 year of experience utilizing relational databases

Responsibilities

• Partner with 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 from large data
• Build machine learning models through all development phases from design to deployment
• Translate complex analytics into tangible business goals

Skills

open source languages

machine learning

relational databases

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

Python

Scala

R

AWS

SQL

Spark

H2O

Job description

Capital One is seeking a Manager, Data Science – US Card (Fraud) to lead fraud modeling initiatives. You will partner with data scientists, software engineers, and product managers to deliver customer‑facing ML products that protect customers and assets.

You’ll work with Python, AWS, H2O, Spark, and other tools to build end‑to‑end models, validate them, and translate complex analytics into actionable business decisions in a high‑scale environment.