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.

The Ideal Candidate is:

• 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.

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 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

Preferred Qualifications:

• 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

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

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 the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.