Senior Manager, Data Science - Model Risk Office
Capital One
Senior Manager, Data Science - Model Risk Office
Job Description
Overview
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 DescriptionThe 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 DescriptionIn 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.
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.
A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
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 7 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 5 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 2 years of experience performing data analytics
At least 2 years of experience leveraging open source programming languages for large scale data analysis
At least 2 years of experience working with machine learning
At least 2 years of experience utilizing relational databases
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
At least 1 year of experience working with AWS
At least 1 year of experience managing people
At least 5 years’ experience in Python, Scala, or R for large scale data analysis
At least 5 years’ experience with machine learning
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.comCapital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Locations
- McLean, Virginia, United States
- Richmond, Virginia, United States
- Chicago, Illinois McLean, VirginiaMcLean
Salary
80,000 - 135,000 USD / yearly
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- data scienceintermediate
- machine learningintermediate
- model risk managementintermediate
- model validationintermediate
- Python/Scala/R programmingintermediate
- statisticsintermediate
- open-source technologiesintermediate
- LLMOpsintermediate
Required Qualifications
- Currently has, or is in the process of obtaining one of the following: Bachelor's Degree in quantitative field plus 7 years data analytics experience, Master's Degree plus 5 years, PhD plus 2 years (experience)
- At least 2 years of experience leveraging open source programming languages for large scale data analysis (experience)
- At least 2 years of experience working with machine learning (experience)
- At least 2 years of experience utilizing relational databases (experience)
- PhD in STEM field plus 4 years of experience in data analytics (preferred) (experience)
- At least 5 years’ experience in Python, Scala, or R for large scale data analysis (preferred) (experience)
Responsibilities
- Partner with cross-functional team to identify and quantify model risks
- Leverage technologies like PyTorch, Hugging Face, LangChain to analyze multi-modal data
- Build machine learning models to challenge deployed champion models
- Validate models across multiple business domains
- Present model risks impact to executives
Benefits
- general: comprehensive, competitive, and inclusive set of health, financial and other benefits
Answer 10 quick questions to check your fit for Senior Manager, Data Science - Model Risk Office @ Capital One.
Related Books and Jobs
Senior Manager, Data Science - Model Risk Office
Capital One
Senior Manager, Data Science - Model Risk Office
Job Description
Overview
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 DescriptionThe 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 DescriptionIn 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.
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.
A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
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 7 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 5 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 2 years of experience performing data analytics
At least 2 years of experience leveraging open source programming languages for large scale data analysis
At least 2 years of experience working with machine learning
At least 2 years of experience utilizing relational databases
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
At least 1 year of experience working with AWS
At least 1 year of experience managing people
At least 5 years’ experience in Python, Scala, or R for large scale data analysis
At least 5 years’ experience with machine learning
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.comCapital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Locations
- McLean, Virginia, United States
- Richmond, Virginia, United States
- Chicago, Illinois McLean, VirginiaMcLean
Salary
80,000 - 135,000 USD / yearly
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- data scienceintermediate
- machine learningintermediate
- model risk managementintermediate
- model validationintermediate
- Python/Scala/R programmingintermediate
- statisticsintermediate
- open-source technologiesintermediate
- LLMOpsintermediate
Required Qualifications
- Currently has, or is in the process of obtaining one of the following: Bachelor's Degree in quantitative field plus 7 years data analytics experience, Master's Degree plus 5 years, PhD plus 2 years (experience)
- At least 2 years of experience leveraging open source programming languages for large scale data analysis (experience)
- At least 2 years of experience working with machine learning (experience)
- At least 2 years of experience utilizing relational databases (experience)
- PhD in STEM field plus 4 years of experience in data analytics (preferred) (experience)
- At least 5 years’ experience in Python, Scala, or R for large scale data analysis (preferred) (experience)
Responsibilities
- Partner with cross-functional team to identify and quantify model risks
- Leverage technologies like PyTorch, Hugging Face, LangChain to analyze multi-modal data
- Build machine learning models to challenge deployed champion models
- Validate models across multiple business domains
- Present model risks impact to executives
Benefits
- general: comprehensive, competitive, and inclusive set of health, financial and other benefits
Answer 10 quick questions to check your fit for Senior Manager, Data Science - Model Risk Office @ Capital One.
Related Books and Jobs