Manager, Data Scientist - Model Risk OfficeHobbsnews • Virginia, Minnesota, United States
Manager, Data Scientist - Model Risk Office
Hobbsnews
- Virginia, Minnesota, United States
- Virginia, Minnesota, United States
À propos
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 have 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 is leading the next wave of disruption at a whole new scale, using the latest 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.
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.
Ideal Candidate
Innovative. You continually research and evaluate emerging technologies, 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, love asking questions, and push 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 and 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.
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 or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
A PhD in a 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 a 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.
Salary Chicago, IL: $179,400 - $204,700
McLean, VA: $197,300 - $225,100
Richmond, VA: $179,400 - $204,700
Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well‑being.
Equal Opportunity & Other Legal Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you require an accommodation, please contact CapitalOne Recruiting at1‑800‑304‑9102 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 CapitalOne’s recruiting process, please send an email to Careers@capitalone.com.
CapitalOne does not provide, endorse, nor guarantee and is not liable for third‑party products, services, educational tools or other information available through this site.
CapitalOne Financial is made up of several different entities. Please note that any position posted in Canada is for CapitalOne Canada, any position posted in the United Kingdom is for CapitalOne Europe, and any position posted in the Philippines is for CapitalOne Philippines Service Corp. (COPSSC).
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Compétences linguistiques
- English
Avis aux utilisateurs
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