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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director of Data Science - **Company:** ClarityPay, Inc. - **Location:** Atlanta, GA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Software Quality, Computer Programming, Information Engineering, Python (Programming Language), Logistic Regression, Standard Sql, Feature Engineering, Model Validation, Pandas, Scikit Learn, Information Technology, Statistics Packages, Xgboost, Performance Monitor, Machine Learning Operations - **Published:** September 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/director-of-data-science-atlanta-ga--4afb2a17-32d4-48af-8685-3fb5af9dfad4 ## About the Role Required * 10+ years of experience in quantitative modeling, with at least 3 years focused on consumer credit risk * Deep, hands-on expertise building PD models - logistic regression, gradient boosting (XGBoost/LightGBM), survival models - in a production lending context * Strong Python (pandas, scikit-learn, statsmodels) and SQL skills; experience deploying models to production environments * Experience with installment loan, personal loan, or BNPL products strongly preferred; point-of-sale or retail credit a plus * Fluency in credit bureau data (Experian, Equifax, TransUnion) and tradeline-level feature engineering * Proven track record building models that improved loss performance or expanded approval rates at a measurable scale * Comfort with the full data science lifecycle: hypothesis feature engineering model training backtesting monitoring * Strong communication skills: ability to translate model outputs into business decisions for non-technical stakeholders * MS or PhD in Statistics, Mathematics, Economics, Computer Science, or related quantitative field (or equivalent experience) Preferred * Experience at a fintech lender, BNPL company, or marketplace lender * Familiarity with CECL / IFRS 9 expected loss frameworks * Experience presenting model frameworks to institutional investors or during ABS securitization diligence * Exposure to fair lending testing (disparate impact analysis, adverse action analysis) * Prior people management experience or demonstrated mentorship of junior data scientists, Benchmarking, Business Model, Capital Markets, Communication Skills, Computer Programming, Computer Science, Consumer Loans, Continuous Improvement, Contract Research Organization (CRO), Credit Risk, Cross-Functional, Customer Experience, Customer Relations, Data Science, Diversity, Documentation Models, Due Diligence, EAD, Economics, Equal Credit Opportunity Act (ECOA), Fair Credit Reporting Act (FCRA), Fair Lending, Finance, Forecasting, Internal Rate of Return (IRR), International Financial Reporting Standards (IFRS), Investment Management, Leadership, Loan Origination, Loans, Loyalty Programs, Mathematics, Mentoring, Model Validation, People Management, Performance Analysis, Performance Management, Performance Modeling, Policy Development, Predictive Modeling, Pricing, Product Design, Product Pricing, Production Systems, Risk, Risk Management, Risk Modeling, Sales, Scorecarding, Statistics, Technical Delivery, Underwriting, Validation Documentation, Warehousing ## Description The Director of Data Science - Credit Risk & Decisioning will own ClarityPay's predictive modeling strategy for consumer credit. You will lead the end-to-end development of Probability of Default (PD) models, Loss Given Default (LGD) frameworks, and behavioral scoring systems that power our origination and portfolio management decisions. You will work at the intersection of risk, pricing, and product - translating raw applicant, bureau, and behavioral data into production-grade models that directly influence approval rates, pricing tiers, and portfolio loss curves. This is a hands-on leadership role: you will write code, build models, and own outcomes, while also mentoring junior data scientists and partnering with Engineering, Finance, and the Capital Markets team., PD & Credit Risk Modeling * Own the full lifecycle of Probability of Default (PD) models for installment loan and BNPL originations - from feature engineering through champion/challenger deployment and ongoing monitoring * Build and maintain LGD and EAD models to support expected loss calculations and pricing optimization * Develop vintage-level loss curves and roll-rate frameworks to forecast portfolio performance across all product terms * Integrate alternative data sources (bureau tradelines, income verification, behavioral signals) to improve predictive lift, particularly for thin-file and non-prime consumers * Design and execute A/B experiments to continuously improve model performance against AUC, KS, and Gini benchmarks Decisioning & Underwriting Infrastructure * Define and maintain decision scorecards and cutoff strategies across product tiers, balancing approval rate, risk appetite, and margin targets * Partner with Pricing to ensure PD output feeds directly into IRR-based pricing frameworks - including Purchase Price and MDR optimization for our merchant network * Build real-time model serving pipelines in collaboration with the Data Engineering team * Drive policy rule development and scorecard governance in alignment with fair lending requirements (ECOA, FCRA) Portfolio Monitoring & Model Risk Management * Establish performance monitoring frameworks: PSI, CSI, and vintage-level deviation tracking versus forecast * Lead model recalibration and rebuild cycles in response to portfolio drift, macro shifts, or product expansion * Produce model documentation and validation artifacts that meet institutional investor and warehouse lender standards * Interface with external model validators and auditors as the company scales its capital markets program Leadership & Cross-Functional Impact * Hire, mentor, and grow a team of data scientists, setting standards for modeling rigor and code quality * Be a thought partner to the CRO, CFO, and Capital Markets team on risk appetite, product design, and investor reporting * Represent ClarityPay's modeling approach to warehouse lenders, ABS investors, and rating agencies during due diligence