Head of Modeling

About the role

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. 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 technology to deliver better outcomes, every time. We are now hiring our first Head of Modeling to own and elevate the decision science that sits at the core of the business. Why this role This is a rare opportunity to stand up a decision-science function from the ground up at a company that already has real volume, real data, and real momentum. You'll set the technical vision, hire and grow the team, put in place the standards, infrastructure, and governance that everything downstream will depend on. This is a leadership role that demands deep technical credibility.

Responsibilities

• Build and lead the internal modeling team - define the team structure and operating model, hire your first internal modelers, and grow a high-caliber decision-science organization. This is your primary mandate.
• End-to-end modeling across the credit and fraud lifecycle - application/underwriting scoring, behavioral and account-management models, line and limit assignment, risk-based pricing, loss forecasting, and collections/recovery optimization.
• Real-time decisioning at the point of sale - models and decision strategies that approve or decline in milliseconds, balancing approval rates, loss, fraud, and customer experience.
• Fraud and identity risk - first-party, third-party, synthetic identity, and account-takeover risk, in close partnership with the fraud and operations teams.
• Merchant- and channel-level risk - modeling and monitoring risk that originates from the merchant side of the POS relationship.
• Model governance and risk management - documentation, independent-style validation practices, ongoing monitoring (PSI/CSI, stability, calibration), and a framework aligned to model-risk-management expectations (e.g., SR 11-7).
• Fair lending and regulatory rigor - disparate-impact and fair-lending testing, adverse-action reason codes and explainability, and partnership with Legal/Compliance on ECOA/Reg B and related obligations.
• Data and infrastructure for modeling - partnering with Engineering and Data to build feature pipelines, deployment paths, and monitoring so models ship reliably and are maintainable over time.
• What you'll build

Requirements

• Required
• Substantial experience (typically 8+ years) building credit risk and/or decision-science models in lending, fintech, or banking, with meaningful time in consumer credit .
• A strong hands-on modeling background across both traditional techniques (logistic regression, scorecards) and modern ML (gradient-boosted trees such as XGBoost/LightGBM/GNN), with the judgment to know when each is appropriate and to hold a team to a high bar.
• Strong Python and SQL , and fluency with the modern modeling and data stack.
• Experience with model governance, validation, and monitoring , and an instinct for documentation and reproducibility.
• Familiarity with the fair-lending and regulatory landscape for consumer lending (ECOA/Reg B, adverse action, model explainability, disparate-impact considerations).
• A track record of building and scaling a modeling or analytics team - hiring strong modelers, developing people, and setting technical standards and culture.
• The technical depth to set direction and evaluate others' work - credible enough to assess inherited models, make build-vs-retain decisions, and dive into the details when it matters.
• Ability to communicate complex modeling concepts clearly to executives, the board, and non-technical partners.
• Preferred
• Direct experience in POS lending, BNPL, installment, or unsecured consumer credit .
• Fraud modeling experience (first-party, third-party, synthetic identity).
• MLOps / production deployment experience - feature stores, model serving, monitoring at scale.
• Exposure to capital markets or funding partners , where loss models inform facility terms and reserves.
• Advanced degree in a quantitative field (statistics, economics, math, computer science, or similar).

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