This role pays more than
83%
of similar roles. Most pay
$130,375–$198,000
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$164k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
798 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 791 roles with salary data.
As a Manager of Data Science in Capital One’s Model Risk Office, you will lead the development and validation of machine learning models across various business domains within Enterprise Services. Your role involves leveraging Python, Conda, AWS, Spark, and other technologies to uncover insights from large datasets and build models that challenge existing “champion” models in production. You will also contribute to the governance framework for next-generation models while presenting model risks to executives with strong interpersonal skills. Ideal candidates are innovative, technically proficient, and experienced in validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection, possessing a deep understanding of statistical methods and big data analytics.
Develop and implement machine learning models to challenge existing production models.
Utilize Python, AWS, Spark, and other technologies to analyze large datasets.
Contribute to the development of a model governance framework for new models.
Present model risks and their business impacts to executive leadership effectively.
Validate various models across multiple business domains within Enterprise Services.
What we're looking for
At least 4 years of experience in building or validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection.
Proficiency in Python and other technologies such as Conda, AWS, Spark.
Strong statistical skills and expertise in machine learning model governance.
Ability to present complex model risks and their business impacts to executives.
Experience with large datasets and the ability to extract meaningful insights.