Senior Director, Data Science & ML Leadership

Capital One; New York (NY); On-site; USD 343,000 - 392,000; Full time

Job summary

Capital One is seeking a Data Scientist to join the US Card Data Science team, working on models across marketing, acquisitions, underwriting, and fraud prevention, using large-scale data to drive decisions.

You will partner with data scientists, engineers, and product managers, apply Python, AWS, Spark, and other tools, and build models from design to deployment, translating complex insights into business value.

Qualifications

• Bachelor's degree in a quantitative field plus 11 years analytics experience
• Master's degree in a quantitative field or MBA with quantitative concentration plus 9 years analytics experience
• PhD in a quantitative field plus 6 years analytics experience
• At least 6 years of experience leveraging open source programming languages for large-scale data analysis
• At least 6 years of experience working with machine learning
• At least 6 years of 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
• 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

Open source languages for large-scale

Machine learning

Relational databases

Education

Bachelor's degree in a quantitative field

Master's degree / MBA with quantitative concentration

PhD in a quantitative field

Tools

Python

Conda

AWS

H2O

Spark

Job description

Capital One is seeking a Data Scientist to join the US Card Data Science team, working on models across marketing, acquisitions, underwriting, and fraud prevention, using large-scale data to drive decisions.

You will partner with data scientists, engineers, and product managers, apply Python, AWS, Spark, and other tools, and build models from design to deployment, translating complex insights into business value.