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.
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