Manager, Data Scientist, Model Risk Office

About the Role

Data is at the center of everything we do. Capital One is a Fortune 200 company and a leader in data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team leading disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records.

The Capital One Model Risk Office is dedicated to safeguarding the company from model failures while simultaneously enhancing decision-making through models, including unique risks associated with Generative AI (GenAI). We strive to achieve optimal results for both Risk Management and the broader Enterprise.

Role Description

In this role, you will:

• 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 — from foundational frameworks (PyTorch, Hugging Face), to orchestration tools (LangChain, Vector Databases) to LLMOps, observability platforms, and more — to reveal the insights hidden within huge volumes of multi-modal data.
• Build machine learning models to challenge “champion models” that are deployed in production today and contribute to the model governance framework for the next generation of models.
• Validate a wide variety of models across multiple business domains within our Enterprise Services division, and flex your interpersonal skills to present how identified model risks could impact the business to executives.

Key Responsibilities

• Identifying and quantifying risks associated with models.
• Leveraging various technologies to reveal insights from multi-modal data.
• Building machine learning models to challenge existing production models.
• Contributing to the model governance framework.
• Validating a wide variety of models across multiple business domains.
• Presenting identified model risks to executives.

Requirements & Tech Stack

The ideal candidate is:

• Innovative: Continually researches and evaluates emerging technologies and seeks opportunities to apply them.
• Creative: Thrives on bringing definition to big, undefined problems and is not afraid to share new ideas.
• Technical: Comfortable with open-source languages and has hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
• Statistically-minded: Has built, validated, and backtested models; knows how to interpret statistical measures; and has experience with clustering, classification, sentiment analysis, time series, and deep learning.
• Data Guru: Skilled in retrieving, combining, and analyzing data from a variety of sources and structures.

Qualifications

Basic Qualifications

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
• A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics.
• A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
• A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics.
• 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.
• At least 1 year of experience utilizing relational or vector databases.

Preferred Qualifications

• PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics.
• At least 1 year of experience working with AWS.
• At least 4 years’ experience in Python, Scala, or R for large scale data analysis.
• At least 4 years’ experience with machine learning, including GenAI.
• At least 4 years’ experience building or validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection.