Staff AI Scientist (Fintech Risk AI/ML)

Location: Mountain View, California, United States

Base pay: $209,500–$283,500 annually, with potential cash bonus, equity rewards, and benefits.

Company

hirify.global is a financial technology platform serving tens of millions of customers through products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Own a money-movement risk capability, including its models, evaluation, production health, and business outcomes.
• Design, train, validate, and deploy real-time models for authorization, funding and settlement, account-level risk, new money products, agentic payments, or risk-ranked operations queues.
• Define and optimize loss, approval rate, hold rate, and time-to-funds outcomes.
• Address delayed and noisy labels, class imbalance, selection bias, reject inference, holdouts, and counterfactual evaluation.
• Detect fraud rings, account takeover, and adaptive attack patterns using graph, sequence, and anomaly-detection methods.
• Partner across ML engineering, risk policy, product, engineering, operations, and finance; establish evaluation standards, mentor scientists and engineers, and lead reviews and hiring.

Requirements

• 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 of owning models that make real-time, high-stakes decisions with measured business impact.
• Deep expertise in gradient-boosted trees, deep learning, anomaly detection, calibration, class imbalance, delayed labels, and selection bias.
• Strong Python and SQL skills, with hands-on large-scale feature engineering using Spark or similar.
• Experience shipping models with ML engineers, including real-time features, serving, retraining, and monitoring.
• Sound experimental judgment in risk settings, including A/B tests, holdouts, and their limitations when attackers can exploit them.
• Clear communication with risk, product, and business leaders.

Nice to have

• Payments risk experience covering card and ACH authorization, funding, chargebacks, returns, or settlement.
• Graph ML or network analysis for fraud rings and collusion.
• Sequence or representation learning on transaction and behavioral data.
• Experience building or evaluating LLM and agentic systems for risk or operations.
• Model risk management and explainability in regulated financial services.
• Experience leading a technical area across teams and mentoring scientists.

Culture & Benefits

• Own models from problem framing through production without being handed a fixed specification.
• Work on millisecond decisions, adaptive attackers, delayed labels, and models that influence their own training data.
• Models directly affect whether small businesses receive money and how quickly.
• Competitive compensation with performance-based rewards, potential cash bonus, equity rewards, and benefits.
• Pay is determined by job-related knowledge, skills, experience, and work location, with regular pay-equity comparisons.