Staff AI Scientist (Fintech Risk AI/ML)

What you'll do

• Own a risk capability’s models, evaluation, production health, and business outcomes; define and improve loss, approval, hold, and time-to-funds metrics.
• Develop and evaluate models for delayed labels, class imbalance, selection bias, and adaptive fraud; establish experimentation and adversarial-testing standards.
• Partner with engineering and cross-functional teams to productionize models, build reusable features and labels, and mentor scientists and engineers.

What they ask for

• 8+ years building and deploying production ML models, including significant experience in fraud, payments, credit, or another adversarial or financial-risk domain.
• MS or PhD in Statistics, Computer Science, Applied Mathematics, Operations Research, or a related field, or equivalent practical experience.
• Track record owning models for real-time, high-stakes decisions with measured business impact; expertise in gradient-boosted trees, deep learning, anomaly detection, and risk-model validation.
• Strong Python and SQL; hands-on large-scale feature engineering with Spark or similar; experience shipping models with ML engineers, including serving, retraining, and monitoring.

Nice to have

• Payments-risk experience; graph ML or network analysis; sequence or representation learning; LLM or agentic systems experience.
• Model risk management and explainability in regulated financial services; technical leadership and mentoring experience.

Pay

USD 209,500 - 283,500 base salary (Mountain View)

Tools

Python, SQL, Spark, gradient-boosted trees, deep learning, anomaly detection, graph ML, LLM

AI focus

Build production AI/ML models for money-movement risk decisions, including fraud detection, funding decisions, risk-ranked investigation queues, and risk for agent-initiated payments. The role is explicitly focused on creating and deploying models, not merely integrating an existing LLM.