Principal Associate - Quantitative Analyst

Overview

Principal Associate - Quantitative Analyst

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 Analyst 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.

This position offers a unique opportunity to be a part of the dynamic Finance organization at Capital One. The Finance organization has been on a technology journey seeking to find ways to leverage technology to drive deeper insights and make the complex simple. We are looking for candidates to help in our journey with modeling, analytical and/or model implementation skills to join our finance team.

Responsibilities and Skills:

- Partner with the various lines of business to develop and enhance Capital Markets modeling and analytical framework, such as deposit predictions, derivatives models and fixed income models.

- Work across Capital One entities to create novel analytical solutions to challenging business problems.

- Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.

- Collaborate in a cross-disciplinary team to build cloud-based solutions grounded in data.

- Identify opportunities to apply quantitative methods or machine learning to improve business performance.

- Apply deep expertise in mathematics, statistical and machine learning methods to generate critical insights and decision frameworks for our business and customers.

- Providing technical guidance to business leadership.

- Communicate technical subject matter clearly and concisely to individuals from various backgrounds.

- Understand and navigate Risk Management Software to enable business analysis.

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:

- Deep understanding of quantitative modeling in relation to finance, deposit behaviors, capital markets and investment portfolio modeling principles.

- Extensive experience in Python or other object-oriented language.

- Ability to clearly communicate modeling results to a wide range of audiences.

- Drive to develop and maintain high quality and transparent model documentation.

- Strong written and verbal communication skills.

- Strong presentation skills.

- Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.

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 5 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 3 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)

Preferred Qualifications:

- Master’s Degree or PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.

- Knowledge of derivative instruments and experience with derivatives modeling.

- Behavior modeling (prepayment or default) experience for complex fixed income products or loans (mortgage/auto/

- 1 years’ experience with Python, C++ or R.

- 1 years’ experience in machine learning.

- 2 years’ experience with data analysis and SQL.

- 1 year of experience manipulating and analyzing large data sets.

- 1 year of system and/or model implementation.

- 1 year of QRM experience.

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: $158,600 - $181,000 for Prin Assoc, Quant 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. 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.