Senior Data Scientist — AI for Data & Assessment

Capital One; San Jose (CA); On-site; USD 177,000 - 201,000; Full time

Job summary

Capital One is seeking a Data Scientist to join the AI for Data team, working with product, tech and design to deliver ML-powered experiences across billions of records.

You will partner with data scientists and engineers to build models, evaluate them, and translate findings into business value, using Python, AWS, Spark, and H2O. This role emphasizes collaboration, experimentation, and scalable deployment across the enterprise.

Qualifications

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

SQL

Deep Learning Frameworks

Natural Language Processing

AWS

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

H2O

Spark

Conda

Job description

Capital One is seeking a Data Scientist to join the AI for Data team, working with product, tech and design to deliver ML-powered experiences across billions of records.

You will partner with data scientists and engineers to build models, evaluate them, and translate findings into business value, using Python, AWS, Spark, and H2O. This role emphasizes collaboration, experimentation, and scalable deployment across the enterprise.