Overview
This role supports the development and management of statistically derived credit risk modeling used for loan and deposit originations, account management, collections, loan loss forecasting, capital plans, and stress testing. The Sr Statistical Modeling Analyst manages statistical model development and implementation both independently and in collaboration with stakeholders.
Pay Range
- Target Pay Range: $128,900.00–$157,500.00 annually
- Full Pay Range: $99,900.00–$186,400.00 annually
Compensation decisions are based on factors such as relevant job-related skills, experience, and education or training. If an offer is made, individual qualifications will be considered. In addition to salary, performance-based compensation incentives are available for the hired applicant (targets vary by role).
Compensation & Benefits
- 401(k) Company Match (up to 3%)
- 4% annual contribution to 401(k)
- Medical, Dental and Vision (family contributions as well)
- PTO Program + Exchange Program
- Tuition Reimbursement Program
- Volunteer time off and donation match
Responsibilities
- Develop, re-develop, and calibrate statistical models using statistical analytical packages, including but not limited to:
- Probability of Default (PD)
- Loss Given Default (LGD)
- Exposure at Default (EAD)
- Models for credit decision scorecards, loss forecasting, reserving, and economic capital use cases
- Support documentation and execution of statistical models under the direction of senior peers and leadership
- Research and apply enhancements to improve accuracy across the existing suite of models, including PD, LGD, EAD, and loan loss forecast models
- Collaborate with business partners and product management to interpret model results and assess the appropriateness of statistical methods and models for business questions and actionable insights
- Participate in annual model reviews and performance testing
- Manage data request and systems testing processes, including gathering and evaluating data for reliability and usability and applying data treatment methods
- Work with senior team members across the advanced credit risk model development life cycle
- Participate in team meetings related to statistical model development
- Deliver regular reports of modeling results, including impacts to originations, servicing, collection, loss mitigation, and asset liquidation strategies and performance
- Maintain thorough knowledge of loan portfolio trends and composition while analyzing and presenting model outputs
- Use data warehouse information and model results to support development of credit risk management strategies
- Identify opportunities for efficiency and effectiveness, including reporting requirements
- Develop and maintain statistical modeling documentation and change control documentation
- Perform other duties as assigned
Requirements
- Master’s degree (or foreign equivalent) in a quantitative discipline such as statistics, math, finance, or economics required; coursework in statistics at either the bachelor’s, master’s or PhD level required
- Minimum 3 years of functional experience in statistical modeling required, including credit risk modeling experience in one or more of the following product areas:
- Real estate secured loan products (mortgage, home equity)
- Auto
- Credit card
- Commercial loan products
- Sound knowledge of statistical modeling concepts, including logistic regression, survival analysis, Markov chain analysis, and time series methodologies
- Experience developing and validating Probability of Default (PD), Exposure at Default (EAD), and Loss Given Default (LGD) models required
- Knowledge of artificial intelligence (AI) and machine learning (ML) tools required
- Knowledge of three or more statistical analytical packages required: SAS, Python, SQL, and R
- Excellent analytical and problem-solving skills required
- Ability to interact with management officials at all levels and other risk and model management personnel throughout the organization required
- Ability to analyze and reconcile large volume of data so it can be summarized and used for management decisions required
Preferred Qualifications
- Experience with statistical modeling for capital planning and stress testing
- Experience with Comprehensive Capital Analysis Review (CCAR), Dodd-Frank Act Stress Testing (DFAST), and Basel Regulatory Capital Framework
- Experience with modeling techniques including logistic regression, multivariate analysis, and Monte Carlo
- Experience communicating complex statistical insights and implications in verbal and written form to support credit union strategy and value creation
EEO Statement
Equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, veteran status, disability, sexual orientation, gender identity, or any other protected status.