Fraud Data Science Manager - Card Risk & ML Leadership

Capital One; Chicago (IL); On-site; USD 176,000 - 201,000; Full time

Job summary

Capital One is seeking a Manager, Data Science – US Card (Fraud) to lead data science initiatives that protect customers from fraud. You will work with cross‑functional teams, building and deploying real‑time models at scale using Python, AWS, Spark, and related tools.

You will design and validate models, interpret results for business decisions, and mentor a team of data scientists focused on fraud prevention across the customer journey.

Qualifications

• Degree requirements with data analytics experience.
• Experience leveraging open source programming languages for large scale data analysis.
• Experience working with machine learning.
• Experience utilizing relational databases.

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 insights from large data sets.
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
• Translate complex work into tangible business goals.

Skills

Data analytics

Machine learning

Statistical modeling

Communication

Team leadership

Education

Bachelor’s Degree in a quantitative field

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

PhD in a quantitative field

Tools

Python

SQL

AWS

Spark

H2O

Conda

Job description

Capital One is seeking a Manager, Data Science – US Card (Fraud) to lead data science initiatives that protect customers from fraud. You will work with cross‑functional teams, building and deploying real‑time models at scale using Python, AWS, Spark, and related tools.

You will design and validate models, interpret results for business decisions, and mentor a team of data scientists focused on fraud prevention across the customer journey.