Lead Data Scientist, US Card Acquisitions

Capital One; Cambridge (MA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One in Cambridge, MA is hiring a Manager, Data Scientist for US Card Upmarket Acquisitions to lead analytics initiatives that shape underwriting and product strategy. You will partner with engineers, product managers, and analysts to translate complex data into actionable business outcomes.

The role emphasizes building scalable models, applying Python, AWS, Spark, and ML tools across large datasets, and guiding a team through the end-to-end model lifecycle with an emphasis on governance

Qualifications

• Bachelor's degree in a quantitative field plus 6 years analytics experience.
• Master's degree in quantitative field or MBA plus 4 years analytics experience.
• PhD in quantitative field plus 1 year analytics experience.
• At least 1 year of experience leveraging open source programming languages for large scale data analysis.
• At least 1 year of experience working with machine learning.
• At least 1 year 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 sets.
• Build machine learning models through all phases from design to deployment.
• Translate complex work into tangible business goals.

Skills

Open-source languages

Machine learning

Relational databases

Cross-functional collaboration

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA

PhD in quantitative field

Tools

Python

Spark

AWS

H2O

Conda

SQL

Job description

Capital One in Cambridge, MA is hiring a Manager, Data Scientist for US Card Upmarket Acquisitions to lead analytics initiatives that shape underwriting and product strategy. You will partner with engineers, product managers, and analysts to translate complex data into actionable business outcomes.

The role emphasizes building scalable models, applying Python, AWS, Spark, and ML tools across large datasets, and guiding a team through the end-to-end model lifecycle with an emphasis on governance