Capital One
Financial Services
SeniorManager,DataScience-ModelRiskOffice
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Manager, Data Science - Model Risk Office at Capital One. Skills: Data Science, Model Risk, Machine Learning, Statistical Modeling, Generative AI. Identify and quantify risks associated with models. Build machine learning models to challenge “champion models””
What You'll Achieve.
Safeguard the company from model failures; Enhance decision-making through models
Industry & Context.
Identify and quantify risks; Challenge conventional thinking; Improve the status quo
What They're Looking For.
Must Have
Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 7 years of experience performing data analytics, 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 5 years of experience performing data analytics, PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics, 2 years of experience leveraging open source programming languages for large scale data analysis, 2 years of experience working with machine learning, 2 years of experience utilizing relational databases
Nice to Have
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics, 1 year of experience working with AWS, 1 year of experience managing people, 5 years’ experience in Python, Scala, or R for large scale data analysis, 5 years’ experience with machine learning
What You'll Do.
Identify and quantify risks associated with models
Build machine learning models to challenge “champion models”
Contribute to the model governance framework
Validate a wide variety of models across multiple business domains
How You'll Work.
Team & Collaboration
Partner with a cross-functional team of data scientists, software engineers, and product managers
Communication Scope
Present how identified model risks could impact the business to executives
Full Job Description
Senior Manager, Data Science - Model Risk Office Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. **Team Description** 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). Leveraging expertise in statistics, software engineering, and business, we strive to achieve optimal results for both Risk Management and the broader Enterprise. We prioritize long-term success by continually investing in future capabilities: acquiring new skills, developing superior tools, and cultivating strong relationships with trusted partners. Our approach involves learning from past errors to develop increasingly robust techniques that prevent recurrence. **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 * Bu
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