The Role
As the Director of Credit and Analytics, you will lead credit risk modeling and portfolio analytics at Billd. Reporting directly to the Chief Operating Officer, this role is responsible for the design, development, and ongoing performance of the decisioning models that govern underwriting, credit limit assignment, and risk-based pricing tiers across Material Financing, Pay App Advance, and Flex Line. Billd's approach is specific to commercial construction: our models assess the subcontractor, the project, and the general contractor together to forecast the subcontractor's cash flow and the timing of payments through the project payment chain. The modeling framework is built on CHAID decision trees, complemented by regression and gradient boosting (XGBoost).
This is a senior, hands-on role that is approximately 90% analytical. You will set the strategic direction for Billd's credit models and carry out the quantitative work directly, from feature engineering and model development through validation and performance monitoring, with AI-assisted analytics integrated into your daily workflow. You will also own all credit reporting to the CEO, the board of directors, and our capital partners.
The ideal candidate combines deep statistical expertise with a practitioner's command of B2B credit, and pairs quantitative rigor with the judgment to interpret model outcomes in context. This role partners closely with Operations and Underwriting, who apply the models to individual transactions, and does not process or approve deals.
Key Responsibilities
- Credit Models: Lead the design, development, and governance of Billd's CHAID-based decisioning models, incorporating regression and gradient boosting (XGBoost) where they improve predictive performance. Manage the full model lifecycle, including development, validation, deployment, ongoing monitoring, and recalibration.
- Cash Flow and Payment Prediction: Model the interaction of contractor, project, and general contractor risk to forecast subcontractor cash flow and payment timing across the construction payment cycle.
- Data and Risk Attributes: Engineer predictive attributes from bank transaction data, commercial and consumer credit bureau data, lien and judgment records, project data, and Billd's internal repayment history. Assess new third-party data sources based on their incremental predictive value.
- Tiers, Limits, and Eligibility: Translate model output into credit tiers, credit limit methodology, and product eligibility for Material Financing, Pay App Advance, and Flex Line, and recommend calibrated changes to credit policy thresholds.
- Credit Modeling Strategy: Define the credit modeling roadmap and its trade-offs among growth, approval rates, and loss performance.
- Credit Reporting: Own all credit reporting to the CEO, board of directors, and capital partners, including portfolio performance and concentration, delinquency and loss trends, vintage and roll-rate analysis, and model performance. Present model changes and recommendations to the Credit Committee.
- Monitoring and Forecasting: Monitor model discrimination, calibration, and stability over time. Lead loss forecasting, scenario analysis, and stress testing, and identify early indicators of portfolio deterioration.
- AI-Driven Analytics: Integrate AI tools into model development, coding, and analysis, and maintain rigorous documentation so results are reproducible and auditable.
- Cross-Functional Partnership: Partner with Operations, Underwriting, Finance, Engineering, and Product to implement model changes in production decisioning systems.
Skills and Qualifications
- Experience: 8+ years in B2B credit, such as commercial credit, small business finance, B2B fintech, or working capital, including 4+ years of hands-on advanced credit modeling and owning production credit risk models.
- CHAID: Experience in CHAID decision-tree modeling is required.
- Statistical Modeling: Expert in statistics and predictive modeling, including regression, gradient boosting (XGBoost), and explainability methods such as SHAP.
- Loss Forecasting and Validation: Experience with probability of default, vintage and roll-rate analysis, survival models, backtesting, and performance monitoring (KS, Gini/AUC, PSI).
- Technical Skills: Advanced Python and SQL. Familiarity with Salesforce and BI tools such as Metabase is a plus.
- AI Proficiency: Daily, hands-on use of AI tools such as Claude or Codex for coding, data analysis, and modeling, including validating AI-generated output.
- Cash-Flow Underwriting: Experience underwriting with bank transaction data is strongly preferred.
- Industry Background: Experience in construction, receivables, factoring, or trade credit is a plus.
- Communication: Demonstrated ability to communicate complex quantitative findings clearly to a board, executive team, or capital partners.
- Judgment and Ownership: Sound judgment in interpreting model results within the broader context of the business and the credit cycle, and the ability to work independently and own projects from start to finish.
- Education: Bachelor's degree in a quantitative field required; master's degree or PhD in statistics, mathematics, economics, data science, or a related field is strongly preferred.
Billd is committed to diversity and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
