Quantitative Model Developer

First Interstate•Bend, OR

About The Position

The Credit Analytics Quantitative Model Developer will have experience in advanced statistical modeling, ideally with a variety of credit portfolios, and will be responsible for both the development and operation of credit risk models including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and Expected Credit Loss (ECL). This position is accountable for model and assumption development and monitoring for the Allowance for Credit Losses estimation in line with the CECL standard, as well as support for stress testing and capital planning as needed. The Credit Analytics Quantitative Model Developer will be considered an expert resource in credit risk modeling, working closely with team members and other stakeholders such as business units and risk management, external auditors, and regulatory agencies.

Requirements

Business knowledge and familiarity with commercial/small business/retail banking products, operations, and processes.
Solid working knowledge of at least two programming languages: Excel VBA, SQL, Oracle SQL, R, Python, SAS, C++. SQL and Python preferred.
Working knowledge of PD/LGD and rating approaches, as well as key industry default and loss data from rating agencies and other vendors.
Ability to communicate technical information in writing.
Familiarity with model risk management best practices and regulatory guidance (OCC 2011/12 SR11-7).
Willing to develop new skill sets such as portfolio theory, macroeconomics (e.g., neoclassical), and extreme value theory.
Time management skills to prioritize multiple tasks in a fast-paced and evolving environment.
Bachelor's Degree in a quantitative field required and Master's Degree in Statistics, Mathematics, Physics, Economics or related field preferred
4-6 years experience in statistical modeling within commercial banks and/or financial institutions required

Nice To Haves

Publication in refereed journals is a plus.

Responsibilities

Provides quantitative support to the Bank’s efforts to manage credit risk in portfolios covering a range of asset classes, and ensure that the PD, LGD, valuation, and ECL models comply with all applicable regulations.
For existing or third-party models, core competency involves understanding the purpose of the models, how they work, how they are used, how well they perform, and what effective challenges are to the current models.
Manages large and complex credit data sets using statistical tools and database technologies.
Designs, builds, and maintains internal and external statistical models to quantify the value of credit risk parameters independently.
Conducts macroeconomic forecasting, performs credit risk forecasting, and incorporates macroeconomic variables in credit risk models.
Performs model calibration, back-testing, sensitivity testing, and stress testing of statistical models.
Presents results to various groups of stakeholders, including senior management.
Delivers high quality documentation and presentations to support and maintain model and library use.
Works with the data governance team to document business requirements, and with information technology to ensure methodologies are accurately implemented in production systems.
Completes ad hoc projects as required.