Head of Modeling

We are now hiring our first Head of Modeling to own and elevate the decision science that sits at the core of the business.

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.

• 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.

Beyond individual models, you'll establish the foundation: a talented internal team and operating model, shared standards for model development and documentation, reproducible pipelines, a validation and monitoring discipline, and a governance posture that holds up to investors, funding partners, auditors, and regulators.

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).

We've described the technical bar above — but at our stage, how you operate matters just as much.

• You thrive in a fast-paced, dynamic startup. Ambiguity, shifting priorities, and limited resources energize rather than paralyze you. You're comfortable making good decisions with incomplete information and revisiting them as you learn.

• You're composed under pressure. When loss trends move, a model misbehaves, or a deadline compresses, you triage calmly, prioritize ruthlessly, and keep the team steady.

• You build through a team, but stay close to the work. You lead by hiring well, setting standards, and developing people — and you stay technically engaged enough to earn the team's respect, pressure-test their work, and step in when the stakes demand it.

• You build for the long run. You balance speed-to-market with the rigor, governance, and reproducibility that make a decision-science function durable — resisting shortcuts that create fragility later.

• Competitive fixed and variable compensation package.

• Comprehensive benefits (medical, dental, vision).

• Collaborative office culture with a strong focus on clients and their customers.

• Opportunities to grow, lead, and shape the future of consumer finance.

• 401k program.

Role Details

• Preference for Atlanta or New York City,

• The chance to build and lead ClarityPay's decision-science function from the ground up.

• Direct impact on the core economics and trajectory of a rapidly scaling business.

• Preference for Atlanta or New York City, but remote locations will be considered.

• This role will report to the Chief Risk Officer

Ready to redefine point-of-sale financing with us? Apply today and join a passionate team committed to making financial clarity a reality.

Salary Range: $150,000 to $200,000, commensurate with experience and qualifications.

ClarityPay is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, age, disability, veteran status, marital status, or any other legally protected status.