We are seeking a highly analytical and business-minded Staff Fraud & Risk Analytics Lead to serve as a key strategic partner across Risk, Operations, Product, and Finance. This role is responsible for building the economic frameworks that enable data-driven decisions around fraud loss tolerance, risk investments, policy optimization, and growth initiatives.
The ideal candidate combines deep expertise in quantitative analysis, risk economics, and financial modeling with the ability to influence executive stakeholders. You will help shape critical business decisions by translating complex risk tradeoffs into clear, measurable financial outcomes and establishing a scalable decision-making framework that balances growth, revenue, and risk.
This position offers a unique opportunity to influence enterprise-level strategy by quantifying the economic impact of risk decisions and creating a trusted source of truth for leadership teams evaluating product expansion, operational investments, and vendor partnerships.
Key Responsibilities
Strategic Risk Economics & Financial Modeling
- Design, enhance, and maintain analytical frameworks that quantify the relationship between fraud losses, risk exposure, customer growth, and revenue outcomes.
- Develop and validate ROI models for new products, policy initiatives, and risk mitigation strategies.
- Assess business tradeoffs by measuring the financial impact of incremental risk acceptance versus anticipated revenue generation.
- Build forecasting methodologies that support loss expectations, scenario planning, and strategic business decisions.
Risk Investment & Vendor Performance Analysis
- Evaluate risk technology and third-party vendor investments through rigorous cost-benefit analysis.
- Quantify the effectiveness of fraud detection tools, controls, and operational processes in reducing losses and improving business performance.
- Establish performance measurement frameworks that ensure risk-related expenditures deliver measurable economic value.
Cross-Functional Business Partnership
- Serve as the primary analytics liaison across Risk, Operations, Finance, Product, and Executive Leadership teams.
- Translate complex risk concepts into financial and business terms that support investment decisions and executive planning.
- Partner with stakeholders to ensure assumptions, models, and recommendations align with business objectives and financial standards.
- Provide analytical support for strategic reviews, governance committees, and executive decision forums.
Decision Support & Executive Insights
- Deliver proactive impact analyses that quantify expected outcomes before policy or product decisions are implemented.
- Develop executive-ready reporting, presentations, and decision frameworks that clearly communicate risks, opportunities, and financial implications.
- Challenge assumptions and recommendations when analysis indicates potential misalignment between expected outcomes and economic realities.
- Drive consistency and transparency in risk-based financial decision making across the organization.
Scalable Analytics & Process Excellence
- Build reusable models, dashboards, and analytical tools that improve efficiency and reduce manual effort across recurring risk assessments.
- Establish data quality standards, model governance processes, and analytical best practices.
- Support the evolution of enterprise risk measurement capabilities through automation and advanced analytics.
Required Qualifications
- Bachelor's degree in Finance, Economics, Statistics, Mathematics, Data Science, Business Analytics, or a related quantitative discipline.
- 5+ years of experience in fraud analytics, risk analytics, financial analysis, business analytics, or related quantitative functions within financial services, fintech, payments, banking, lending, or technology environments.
- Strong expertise in financial modeling, forecasting, scenario analysis, and ROI evaluation.
- Demonstrated experience quantifying business tradeoffs involving risk, revenue, operational costs, or investment decisions.
- Advanced SQL skills and experience working with large-scale datasets.
- Strong analytical problem-solving abilities with exceptional attention to detail and data accuracy.
- Proven ability to communicate complex analytical findings to both technical and non-technical audiences.
- Experience influencing cross-functional stakeholders and senior leadership through data-driven recommendations.
Preferred Qualifications
- Experience in fraud strategy, fraud operations, fraud prevention, credit risk, chargebacks, payments risk, or trust & safety domains.
- Knowledge of risk-adjusted economics, loss forecasting, and portfolio performance measurement.
- Prior exposure to finance, accounting, FP&A, treasury, or enterprise risk management functions.
- Experience supporting executive-level decision making within high-growth fintech, financial services, marketplace, or technology organizations.
- Familiarity with stress testing methodologies, scenario modeling, and sensitivity analysis.
- Experience with business intelligence and visualization platforms such as Tableau, Looker, Power BI, or similar tools.
- Experience working with cloud-based analytical environments and modern data platforms.
- Exposure to predictive analytics, experimentation frameworks, or advanced statistical modeling techniques.
What Success Looks Like
- Trusted advisor for evaluating the financial viability of risk-related business decisions.
- Reliable owner of frameworks that quantify the economic impact of fraud, risk, and operational investments.
- Consistent provider of executive-ready insights that balance growth objectives with sustainable risk management.
- Driver of scalable analytical solutions that improve decision velocity, transparency, and business performance.
Fraud Analytics, Risk Analytics, Fraud Strategy, Risk Economics, Financial Modeling, ROI Analysis, Loss Forecasting, Fraud Prevention, Fintech, SQL, Tableau, Looker, Executive Reporting, Business Intelligence, Risk Management, Payments Risk, Credit Risk, Data Analytics, Financial Planning, Decision Science, Strategy Analytics.