Senior Associate, Quantitative Analyst - Mlro

Capital One Capital One · Banking · New York, NY +1

Develops and enhances market and counterparty credit risk models for Capital Markets, focusing on Value-at-Risk (VaR), Greeks, PFE, and CVA. The role involves creating novel analytical solutions, applying quantitative methods for business performance improvement, and building cloud-based solutions. Requires strong quantitative analysis skills, proficiency in scripting languages like Python, and experience in econometric analysis and machine learning.

What you'd actually do

  1. Partner with the various lines of business to enhance modeling and analytical framework. Particularly focused on Value-at-Risk (VaR), Greeks (sensitivities), Potential Future Exposure (PFE), and Credit Value Adjustment (CVA)
  2. Work across Capital One entities to create novel analytical solutions to the challenging business problems
  3. Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies
  4. Collaborate in a cross-disciplinary team to build cloud-based solutions grounded in data
  5. Identify opportunities to apply quantitative methods to improve business performance

Skills

Required

  • Statistical or econometric modeling
  • Linear and logistic regression
  • Programming in R, Python or SQL
  • Presenting statistical concepts and research results to non-statistical audience
  • Analysis and management of large datasets (>1M records)
  • Quantitative analysis methods in relation to derivatives
  • Python
  • Ability to clearly communicate modeling results to a wide range of audiences
  • Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.
  • Demonstrated track-record in econometric analysis.

Nice to have

  • Survival analysis modeling
  • Time-series analysis
  • Panel data (longitudinal data or cross-sectional time-series data) analysis
  • Cross-sectional data analysis
  • Machine learning
  • Python, R or other statistical analyst software

What the JD emphasized

  • quantitative analysis
  • modeling
  • statistical modeling
  • econometric analysis
  • machine learning

Other signals

  • developing market and counterparty credit risk models
  • quantitative analysis methods in relation to derivatives
  • statistical or econometric modeling
  • machine learning