Principal Data Scientist – Card & Payments

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 Business Cards & Payments Data Science team. You’ll partner with engineers, product managers, and stakeholders to deliver data-driven solutions that support credit underwriting and card product strategies.

You’ll work with Python, AWS, Spark, and related tools to build models from design through deployment, translate complex findings into tangible business goals, and contribute to a data-driven culture with

Qualifications

• Bachelor’s degree in a quantitative field plus 5 years of data analytics experience or equivalent.
• Master’s degree in a quantitative field or MBA with quantitative concentration plus 3 years of data analytics.
• PhD in a quantitative field.

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 the insights hidden within huge volumes of numeric and textual data.
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
• Translate the complexity of your work into tangible business goals.

Skills

Python

Scala

R

SQL

AWS

Machine learning

Education

Bachelor's Degree in a quantitative field + 5 years data analytics

Master’s Degree in a quantitative field or MBA with quantitative concentration + 3 years data analytics

PhD in a quantitative field

Tools

AWS

Spark

H2O

Python

Job description

Capital One in New York, NY is seeking a Principal Associate, Data Scientist for the Business Cards & Payments Data Science team. You’ll partner with engineers, product managers, and stakeholders to deliver data-driven solutions that support credit underwriting and card product strategies.

You’ll work with Python, AWS, Spark, and related tools to build models from design through deployment, translate complex findings into tangible business goals, and contribute to a data-driven culture with