Lead Data Scientist, Alternative Data Strategy (Hybrid)

Capital One; Richmond (VA); Hybrid; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Manager, Data Scientist for the Alternative Data Strategy team in a hybrid role. You will collaborate with data scientists, engineers, and product managers to build scalable ML models using Python, AWS, Spark, and H2O across large datasets.

You’ll evaluate new data sources, engineer features, and navigate risk governance while driving business outcomes and cross-functional collaboration—at a Fortune 200 scale.

Qualifications

• Bachelor’s degree in a quantitative field plus 6 years of data analytics experience OR Master’s with 4 years OR PhD with 1 year of data 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 and 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 datasets
• 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
• Adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location

Skills

Python

AWS

Spark

H2O

Relational DBs

Machine learning

Text data analysis

Conda

Education

Bachelor’s degree in a quantitative field

Master’s degree in a quantitative field or MBA with a quantitative concentration

PhD in a quantitative field

Tools

Conda

Job description

Capital One is seeking a Manager, Data Scientist for the Alternative Data Strategy team in a hybrid role. You will collaborate with data scientists, engineers, and product managers to build scalable ML models using Python, AWS, Spark, and H2O across large datasets.

You’ll evaluate new data sources, engineer features, and navigate risk governance while driving business outcomes and cross-functional collaboration—at a Fortune 200 scale.