Senior Data Scientist, Personalization & Recommendations

Capital One; McLean (VA); On-site; USD 136,000 - 155,000; Full time

Job summary

Capital One is seeking a Senior Associate Data Scientist to advance recommendation and personalization systems. You will partner with cross-functional teams across data science, software engineering, and product management to deploy high-scale ML solutions.

The role emphasizes building models through all stages, using Python, AWS, Spark, and other tools, with a focus on customer-centric decisions and measurable impact.

Qualifications

• Bachelor’s degree in Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field plus 2 years of data analytics experience.
• Master’s degree in a quantitative field or an MBA with a quantitative concentration or equivalent.

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
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Data analytics

Cross-functional collaboration

Machine learning

Cloud computing

Education

Bachelor's degree in quantitative field

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

Tools

Python

Scala

R

SQL

AWS

Spark

H2O

Conda

Job description

Capital One is seeking a Senior Associate Data Scientist to advance recommendation and personalization systems. You will partner with cross-functional teams across data science, software engineering, and product management to deploy high-scale ML solutions.

The role emphasizes building models through all stages, using Python, AWS, Spark, and other tools, with a focus on customer-centric decisions and measurable impact.