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Senior Manager, Quantitative Analysis - Model Risk Office

$229.9k - $262.4k
Full-time

Capital One

Senior Manager, Quantitative Analysis - Model Risk Office

At Capital One 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 Quantitative Senior Manager 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 cloud 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.

As part of the Model Risk function, you will partner with high-performing model development teams and model risk teams responsible for advance Capital One’s Loan Loss Forecasting and Allowance for Credit Losses (ACL) framework.

Responsibilities and Skills:
- Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools.
- Develop alternative model approaches to assess model design and advance future capabilities.
- 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.

Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Successful candidates would possess:
- Demonstrated track-record in modeling and experience utilizing model estimation tools such as Python or R.
- Ability to clearly communicate modeling or validation results to management, model risk officers, regulators and other modelers.
- Drive to continuously improve all aspects of their work in a collaborative fashion.
- Experience in machine learning.
- Strong communication skills with the ability to quickly understand existing models and new requirements/business needs.

- Experience working with Agile development methodologies.

- Strong grasp of econometric theory and methodologies.

- Desire to remain on the leading edge of analytical technology with a passion for the newest and most innovative tools.

Basic Qualifications:

  • Currently has, or is in the process of obtaining one of the following with an exception that the required degree will be obtained on or before the scheduled start date:

    • 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 in quantitative 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 in quantitative analytics

  • At least 5 years of experience in each of the following skills through education or experience:

    • Statistical or econometric modeling

    • Linear and logistic regression

    • Programming in R, Python, or SQL

    • Presenting statistical concepts and research results to non-statistical audience

  • At least 5 years of experience in at least 3 of the following skills:

    • Survival analysis modeling

    • Time-series analysis

    • Panel data (longitudinal data or cross-sectional time-series data) analysis

    • Cross-sectional data analysis

    • Machine learning

    • Analysis and management of large datasets (>1M records)

Preferred Qualifications:

  • 6 years of experience with Python, R or other statistical analyst software

  • 6 years of experience in statistical modeling or regression analytics or machine learning

  • 2 years of experience managing people

  • 6 years of experience in models for Commercial Bank

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.

McLean, VA: $229,900 - $262,400 for Sr Mgr, Quantitative AnalysisAnytown, IL: $209,000 - $238,500 for Sr Mgr, Quantitative AnalysisRiverwoods, IL: $209,000 - $238,500 for Sr Mgr, Quantitative Analysis

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.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

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.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. 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 for employment 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 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 View phone number on aiapply.co or via email at View email address on aiapply.co. 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 View email address on aiapply.co

Capital 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).

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