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.

Responsibilities

• Merchant Risk: Underwriting and Approval Rate Optimization
• 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.
• 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/Mastercard program thresholds.
• Transaction Risk: Real-Time Fraud and Anomaly Detection
• Build card testing detection models using high-frequency, low-value authorization pattern recognition - with explicitly defined precision and recall targets. Flag the pattern before the fraudster pivots to high-value transactions.
• Use unsupervised approaches - isolation forests, autoencoders - for emerging fraud typologies that have no labeled training data yet, layered alongside supervised models for known patterns. Unknown unknowns are where bust-out fraud lives before it gets named.
• AML and Transaction Monitoring
• Partner with the Compliance team to evolve rule-based transaction monitoring into a hybrid system: rules for explainability and auditability, ML for alert prioritization, coverage, and signal quality.
• Build structuring detection models that complement velocity rules with behavioral pattern recognition: temporal sequencing, counterparty relationships, amount clustering, and network-level coordination signals.
• Own the quantitative side of SAR quality: alert-to-SAR conversion rate, filing timeliness (regulat

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