Risk Data Scientist

About the Role

Forward processes payments for thousands of merchants across dozens of partner platforms.

The Risk Data Scientist's job is to build the model-driven intelligence layer that replaces static rules with adaptive, evidence-based decisioning across three risk domains: merchant underwriting and approval optimization, real-time transaction fraud and anomaly detection, and AML/transaction monitoring and SAR prioritization. You will own the full modeling lifecycle - from problem framing and feature engineering to training, validation, deployment, monitoring, and regulatory governance.

This is applied ML in a high-stakes, regulated financial context. Models you build will directly determine approval rates, fraud loss rates, chargeback exposure, and SAR filing quality. They will be scrutinized by bank sponsors, card networks, and regulators. The work demands both technical rigor and regulatory fluency: someone who understands why a gradient boosting ensemble outperforms logistic regression on imbalanced fraud data AND why SHAP explainability is a compliance requirement under ECOA adverse action rules.

Forward is early in this journey. The person who takes this role will define the ML architecture, build the model governance framework, and set the standard for how Forward uses machine learning in regulated financial services.

Key Responsibilities

Merchant Risk: Underwriting and Approval Rate Optimization

• Build and own the merchant risk scoring model - the centerpiece of Forward's move from static rules to model-based decisioning. This model drives tier assignment (auto-approve, conditional approve, RFI, decline) for every merchant application, replacing hand-coded thresholds with evidence-based probability scores.
• Design vertical-specific scoring variants for contractor/home services, healthcare, and hospitality - each with distinct chargeback profiles, fraud patterns, and volume distribution that a single generalized model cannot capture.
• Build bust-out fraud detection models that catch the full behavioral arc: months of normal volume cultivating trust, followed by rapid drawdown. Use graph neural network (GNN) approaches to surface shared infrastructure - phone numbers, IP ranges, device fingerprints, bank accounts - across seemingly unrelated merchants in the same fraud ring. At scale, GNN-based ring detection catches 40%+ more coordinated fraud than models that evaluate merchants in isolation.
• Integrate model scores as dynamic inputs to Taktile rules - not as replacements for rules, but as continuous risk signals that allow thresholds to flex based on model confidence. A merchant with a 95% confidence clean score should not face the same velocity friction as one at 60%.
• Measure outcomes in business terms: approval rate lift, false positive rate reduction, and chargeback rate per merchant segment vs. Visa/Mast