Head of Math and Science

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.
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
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.
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.
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. 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. 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., 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.
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.
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).
Ambiguity, shifting priorities, and limited resources energize rather than paralyze you. You balance speed-to-market with the rigor, governance, and reproducibility that make a decision-science function durable — resisting shortcuts that create fragility later.
Comprehensive benefits (medical, dental, vision).

• Collaborative office culture with a strong focus on clients and their customers.
• 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
Apply today and join a passionate team committed to making financial clarity a reality.
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.