Senior Data Scientist - Cards & Payments

Capital One National Association; 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 Business Cards & Payments. You will build ML models to support credit underwriting and product strategies across the card lifecycle, collaborating with business stakeholders, product, and engineering teams.

You will work with a broad stack including Python, AWS, Spark, and H2O to extract insights from large datasets and translate complex analyses into actionable business decisions.

Qualifications

• Bachelor’s degree in a quantitative field plus 5 years of data analytics experience.
• Master’s degree in a quantitative field (Statistics, Economics, OR, Analytics, Mathematics, CS) or MBA with a quantitative concentration plus 3 years of data analytics experience.
• PhD in a quantitative field (Statistics, Economics, OR, Analytics, Mathematics, CS)

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

R

Scala

SQL

AWS

Machine learning

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA with quantitative concentration

PhD in quantitative field

Tools

AWS

H2O

Spark

Job description

Capital One in New York, NY is seeking a Principal Associate, Data Scientist for Business Cards & Payments. You will build ML models to support credit underwriting and product strategies across the card lifecycle, collaborating with business stakeholders, product, and engineering teams.

You will work with a broad stack including Python, AWS, Spark, and H2O to extract insights from large datasets and translate complex analyses into actionable business decisions.