Director of Data Science

Job description

**About Us** At ClarityPay, we're redefining the point-of-sale credit market to bring more value to merchants. Based in NYC and Atlanta, our fast-growing fintech empowers large merchants with configurable “Pay-Over-Time” tools— including monthly installments, BNPL, and revolving products. We solve complex credit challenges with speed, precision, and intelligence—combining deep expertise with advanced tech to deliver better outcomes, every time. Our clients rely on us to help them serve their customers, grow, and build loyalty. Our values guide everything we do: we put merchants first, stay data-driven, always know the why, learn relentlessly, and win together as a team. This clarity of purpose fuels our commitment to delivering exceptional customer experiences at speed and scale. **About The Role** 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. **KEY RESPONSIBILITIES** **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 **WHAT WE'RE LOOKING FOR** **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 *ClarityPay is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*