Lewis James Professional

Quantitative Analyst

Lewis James Professional is a woman-owned staff augmentation, project consulting and direct hire search and placement firm. We are currently seeking a Quantitative Analyst for a contract opportunity with a financial service client.

Responsibilities:

  • Support the Credit Risk Administration Group with the development, validation, and maintenance of CECL, stress testing, and risk rating models, including development of quantitative and qualitative assessment methodologies, selection and integration of credit and econometric data, scenario analysis, model output and validation, back-testing methodologies, and model documentation.
  • Interact with key stakeholder groups such as Accounting, Treasury, Credit Risk Administration, Model Risk Management, and Information Technology in the design, development, and ongoing usage of models.
  • Monitor the performance and calibration of existing models.
  • Develop and apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data sets from multiple sources (including consumer, mortgage, and commercial credit information and economic forecasts) to develop credit risk models for CECL, stress testing, risk rating, and other credit risk related initiatives.
  • Derive model assumptions that are well reasoned and supportable.
  • Implement models in code in a transparent and easily maintainable way.
  • Comprehensively and clearly document all modeling or analysis work that meets internal, GAAP, and regulatory requirements; translate model theory and related results for non-quantitative audiences.
  • Develop and support strong controls for the model implementation framework and maintain related documentation.
  • Support independent model validation process, internal and external audits, and regulatory reviews.
  • Interact with model owner/users, validators, and regulators to address model issues and remediation actions.
  • Work on various ad hoc quantitative, modeling, and programming assignments.

Qualifications:

  • PhD or master’s degree in Statistics, Econometrics, Mathematics or related quantitative field.
  • A Bachelor’s degree in a quantitative field with additional certifications or experience may be considered.
  • Must have advanced quantitative statistical modeling skills (Regression, Time-Series, Data Mining, Survival Analysis, Sensitivity, Back testing, etc.)
  • Experience with at least one of the following software packages: R, SAS, SQL, Python
  • Strong analytical and critical thinking skills with high attention to detail and accuracy
  • Excellent verbal, written, and interpersonal communication skills.

Preferred Experience:

  • 2 or more years of model development or validation experience, particularly in credit risk or stress testing.
  • Working knowledge of R, SAS, and SQL.
  • Working knowledge of Generally Accepted Accounting Principles (GAAP), Basel III, Dodd-Frank Act Stress Testing, CCAR, and bank accounting/regulatory reporting requirements.
  • Ability to clearly articulate, in writing or orally, ideas, analytic insights, and recommendations to both technical and non-technical audiences, including an executive audience.
  • Ability to use advanced statistical and mathematical software to perform descriptive, predictive, and prescriptive analysis leveraging a variety of statistical techniques (such as segmentation, logistic regression, sensitivity analysis, and machine learning)
  • An ability to identify key problems, conduct in-depth research, and articulate well-reasoned solutions.
  • Knowledge of R, SAS, or SQL
  • Proficiency in the use of Microsoft Office with advanced experience in Excel
  • Familiarity with software version control systems, such as Git

For immediate consideration, please click “Apply” and use Job Code BHJOB11837_2584. You may also send a copy of your resume to: resumes@lewisjamesprofessional.com and enter only the following job code in the subject line: BHJOB11837_2584. Lewis James Professional is an Equal Opportunity Employer. M/F/D/V