Principal Data Scientist, Personal Loan Analytics

Capital One; New York (NY); On-site; USD 177,000 - 201,000; Full time

Job summary

Capital One in New York, NY, is seeking a Principal Associate Data Scientist for the Personal Loan group. You will work with a cross-functional team to deploy models that optimize loan offers and pricing at scale.

You will use Python, Spark, AWS, and related tools to build models through all development stages, interpret results, and translate findings into business impact for customers.

This role offers a competitive salary with location-based ranges and eligibility for incentives.

Qualifications

• Bachelor's degree or higher in a quantitative field with analytics experience
• Master's degree or MBA with quantitative concentration plus analytics experience
• PhD in a quantitative field

Responsibilities

• Collaborate with cross-functional 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 phases of development, from design through training, evaluation, validation, and deployment.
• Translate the complexity of your work into tangible business goals.

Skills

Python

Spark

SQL

R

AWS

H2O

Conda

Machine learning

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

Job description

Capital One in New York, NY, is seeking a Principal Associate Data Scientist for the Personal Loan group. You will work with a cross-functional team to deploy models that optimize loan offers and pricing at scale.

You will use Python, Spark, AWS, and related tools to build models through all development stages, interpret results, and translate findings into business impact for customers.

This role offers a competitive salary with location-based ranges and eligibility for incentives.