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# Manager, Data Science - Model Risk Office

- **Company:** Capital One Financial Corporation
- **Location:** McLean, VA, United States
- **Salary:** $197,300.0 - $225,100.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, Big Data, Relational Databases, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Open Source Technology, Presentation Programs, Sentiment Analysis, Cloud Platform System, Deep Learning, Model Validation, Information Technology, Data Analytics, Xgboost
- **Published:** September 1, 2026
- **Apply:** https://dejobs.org/x/x/7358CFD8D69B414389F0A5E31190B6BA/job/

## About the Role

* Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
 * Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
 * Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.
 * Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
 * Statistically-minded. You've built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
 * A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science., * 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 databases, * 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

## Description

Capital One is selectively recruiting for a Manager for a Model Validation team. The individual would report to the Model Risk Office and work closely with the business groups. This position is responsible for validating payment network business models, including fraud risk, Anti-Money Laundering (AML), Counterparty risk, and financial models. Strong communication skills are essential to effectively engage with a diverse group of stakeholders, irrespective of their technical background., * Perform independent model validations for payment network models, including fraud, AML, counterparty and financial models in accordance with regulatory guidanceSR 11-7 and internal model risk policy and standards.

 * Validate fraud and AML modeling approaches, including:
 * Rule-based systems and thresholds
 * Statistical models
 * Machine learning models, (e.g., Gradient Boosting, Random Forecast)
 * Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
 * Understand relevant business processes and portfolios associated with model use
 * Understand technical issues in econometric, statistical, and machine learning modeling and apply these skills toward developing models and assessing model risks and opportunities
 * Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management
 * Maintain the efficiency and accuracy of our models through continuous improvement and application of best practices
 * Develop and maintain high quality and transparent documentation
 * Leverage the latest open-source technologies and tools to identify areas of opportunity in our existing framework


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