Senior Quantitative Risk Strategist

Binance.US is America’s home to buy, trade, and earn digital assets. As a licensed and regulated U.S. crypto platform, we provide secure, reliable access to more than 190 of the world’s most popular cryptocurrencies, all with some of the lowest fees in the industry. We’re a remote-first team of innovators building the bridge between traditional finance and Web3, helping bring financial freedom within reach for all. To learn more, visit www.binance.us. All roles supporting Binance.US are employed via BAM Management US Holdings Inc. or BAM Management Canada Holdings Inc.

The Senior Quantitative Risk Strategist is part of the Product organization and serves as a senior individual contributor responsible for designing, developing, validating, and optimizing quantitative risk models and analytical frameworks that protect Binance.US customers, payment systems, and operations from fraud, financial crimes, account compromise, and payment-related losses.

This role will act as the quantitative lead for payments risk, partnering closely with Product, Risk, Compliance, Engineering, Data, and Operations teams to develop scalable, data-driven risk solutions across customer onboarding, account funding, trading, and withdrawal experiences. The ideal candidate combines deep expertise in predictive modeling and risk analytics with practical experience managing fraud, ACH, card, and transaction risk within highly regulated financial services, fintech, or cryptocurrency environments.

Success in this role requires balancing customer growth, conversion, and user experience with loss prevention, regulatory obligations, and operational efficiency.

• Design, develop, deploy, and continuously improve predictive risk models, scoring methodologies, and decision frameworks across ACH deposits, wire transfers, debit card funding, withdrawals, account security, fraud, and cryptocurrency-related risk domains.

• Build customer-level, transaction-level, and portfolio-level models that predict fraud losses, payment returns, chargebacks, account compromise, and emerging risk events.

• Develop risk segmentation frameworks that dynamically assess customer behavior throughout the customer lifecycle, including onboarding, funding, trading activity, and withdrawals.

• Apply advanced statistical methods, machine learning techniques, anomaly detection, and large-scale data analysis to identify evolving fraud patterns, abuse vectors, and operational vulnerabilities.

• Quantify and monitor risk across payment rails, including ACH, wire, card, and other funding methods.

• Develop forecasting methodologies for payment losses, ACH return rates, chargebacks, fraud exposure, and emerging risk trends.

• Establish key risk indicators (KRIs), operational thresholds, portfolio health metrics, and executive reporting frameworks.

• Evaluate the impact of new products, payment methods, customer segments, and growth initiatives through rigorous quantitative risk assessment.

• Partner with Product and Engineering teams to embed model-driven decisioning throughout the customer journey.

• Design, test, and optimize risk rules, controls, decision engines, and transaction monitoring systems to maximize fraud prevention while minimizing customer friction.

• Lead champion/challenger testing and experimentation frameworks to measure the effectiveness of risk controls and drive continuous improvement.

• Quantify tradeoffs between fraud loss, customer conversion, operational efficiency, and business growth to support data-driven decision making.

• Conduct deep-dive investigations into material fraud events, payment losses, suspicious activity, and operational incidents, identifying root causes and recommending corrective actions.

• Support model governance, validation, audit, and regulatory review activities through comprehensive documentation, monitoring, and control frameworks.

• Establish model performance monitoring, calibration, and backtesting processes to ensure ongoing effectiveness and compliance.

• Serve as a trusted advisor to senior leadership on payments risk, fraud analytics, financial crime risk, and cryptocurrency-related risk trends.

• 8+ years of experience in quantitative risk analytics, fraud science, payments risk, machine learning, data science, or risk modeling within fintech, financial services, payments, banking, or cryptocurrency organizations.

• Advanced degree in Statistics, Mathematics, Economics, Finance, Computer Science, Physics, Operations Research, Data Science, or a related quantitative discipline. Equivalent industry experience building and deploying production risk models will be strongly considered.

• Advanced proficiency in SQL and strong experience with Python, Spark, R, or comparable analytical and modeling technologies.

• Demonstrated success designing, deploying, validating, and monitoring predictive risk models in production environments.

• Deep understanding of ACH payments, return codes, chargebacks, account takeover risk, first-party fraud, synthetic identity fraud, mule activity, payment abuse, and transaction risk management.

• Experience developing portfolio-level forecasting models for fraud losses, payment returns, chargebacks, or credit-related risk outcomes.

• Strong knowledge of AML regulations, sanctions compliance, KYC/KYB requirements, and financial crime risk management.

• Experience supporting model governance programs, audit reviews, regulatory examinations, and independent model validation activities.

• Familiarity with blockchain analytics platforms, cryptocurrency transaction monitoring solutions, and digital asset risk indicators.

• Strong communication skills with the ability to translate complex quantitative findings into actionable business recommendations for both technical and executive audiences.