Lead Data Scientist, Bank Claims & Protection

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

Job summary

Capital One is seeking a Data Scientist to lead the claims data science effort within the Bank Customer Protection team. You will guide a small team of data scientists, balance stakeholder priorities, and remain hands-on with data exploration, model development, and end-to-end deployment.

You will work with a broad stack including Python, Spark, AWS, and LangChain to build scalable models and deliver real-time decisioning across billions of records, driving fraud prevention and improved customer

Qualifications

• Bachelor's degree in a quantitative field plus 7 years analytics experience
• Master's degree in a quantitative field or MBA with quantitative concentration plus 5 years analytics experience
• PhD in a quantitative field plus 2 years analytics experience
• At least 2 years of experience leveraging open source programming languages for large scale data analysis
• At least 2 years of experience working with machine learning
• At least 2 years of experience utilizing relational databases

Responsibilities

• Lead cross-functional data science initiatives with researchers, engineers, analysts, and product managers
• Leverage a broad stack of technologies — Python, Spark, AWS, LangChain — to operationalize insights from large data volumes
• Build machine learning models through all phases of development from design to deployment
• Lead individuals by removing ambiguity and shaping the roadmap of the space

Skills

Leadership

Creative

Technical

Data analytics

Education

Bachelor's with quantitative field

Master's with quantitative field

PhD with quantitative field

Tools

Python

Spark

AWS

LangChain

Ray

Job description

Capital One is seeking a Data Scientist to lead the claims data science effort within the Bank Customer Protection team. You will guide a small team of data scientists, balance stakeholder priorities, and remain hands-on with data exploration, model development, and end-to-end deployment.

You will work with a broad stack including Python, Spark, AWS, and LangChain to build scalable models and deliver real-time decisioning across billions of records, driving fraud prevention and improved customer