Principal Data Scientist: Model Risk & ML Leadership

Capital One; McLean (VA); Presencial; USD 162 000 - 185 000; Tempo integral

Resumo da oferta

Capital One is seeking a Senior Data Scientist to join the Model Risk Office. You will partner with data scientists, engineers, and product managers to quantify model risks and leverage Python, AWS, Spark, and H2O to extract insights from billions of records.

You will lead ML model development, validation, and governance across multiple domains, presenting results to executives and driving risk-informed decisions at scale.

Qualificações

• Bachelor's Degree in a quantitative field plus 5 years analytics experience.
• Master's Degree in a quantitative field or MBA with quantitative concentration plus 3 years analytics experience.
• PhD in a quantitative field.
• Master’s Degree in STEM plus 3 years data analytics, or PhD in STEM.
• At least 1 year of experience working with AWS.
• At least 3 years’ experience in Python, Scala, or R.
• At least 3 years’ experience with machine learning.
• At least 3 years’ experience with SQL.

Responsabilidades

• Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models.
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal insights from large datasets.
• Lead teams of data scientists who build machine learning models and contribute to the model governance framework.
• Lead validation of models across multiple business domains and communicate implications to executives.

Conhecimentos

Python

R

Scala

Machine Learning

Statistics

Formação académica

Bachelor's Degree in a quantitative field

Master's Degree in a quantitative field or MBA with quantitative concentration

PhD in a quantitative field

Ferramentas

Spark

H2O

AWS

Descrição da oferta de emprego

Capital One is seeking a Senior Data Scientist to join the Model Risk Office. You will partner with data scientists, engineers, and product managers to quantify model risks and leverage Python, AWS, Spark, and H2O to extract insights from billions of records.

You will lead ML model development, validation, and governance across multiple domains, presenting results to executives and driving risk-informed decisions at scale.