This role pays more than
87%
of similar roles. Most pay
$137,825–$207,500
— the shaded band above.
At the midpoint, this role pays about
$235k
versus about
$173k
for comparable roles.
Based on 239 similar postings.
Employer
About Intuit
Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology
Intuit currently has
174 open roles
on FindRole.
Listed pay typically runs
$202,500–$274,000
across 152 roles with salary data.
As a Senior Staff Fraud & Risk Analyst on the Lending Fraud Policy team, you will own end-to-end fraud strategy across Term Loan, Line of Credit, and Revenue Based Financing products. You will be responsible for setting policy across the full lifecycle, designing and tuning detection systems for stolen identities, account takeovers, first-party fraud, and bust-out rings. Your daily work involves leading complex forensic investigations, partnering with Data Science on ML models and AI Agent automation, and collaborating with Finance and Legal to manage loss forecasting and regulatory compliance. To succeed, you must possess an advanced degree in a quantitative field and expertise in SQL, Python, Databricks, Hive, and Hadoop. You will solve critical fraud problems within the money movement platform by balancing risk-informed decisions with seamless customer experiences across various lending products.
Own end-to-end lending fraud strategy and policy across Term Loan, Line of Credit, and Revenue Based Financing products.
Design and tune detection systems for synthetic identity, account takeover, first-party fraud, and bust-out rings.
Lead forensic investigations into sophisticated multi-product fraud rings to drive immediate containment and permanent policy updates.
Partner with Data Science to develop ML detection models and AI-powered automation for case review and policy execution.
Develop risk strategies for new lending initiatives by balancing revenue growth against potential fraud losses.
Collaborate with Finance to manage loss forecasting, reserves, and unit economics for informed decision-making.
Coordinate with Risk Operations to design case review workflows and enforce fraud holds during money movement.
Communicate complex fraud strategy decisions and trade-offs to senior leadership, financial partners, and regulators.
What we're looking for
MS/PhD in a quantitative field such as Statistics, Mathematics, Economics, Operations Research, or Finance.
7+ years of experience in fraud analytics, credit risk, or data science.
Fintech and lending experience with hands-on ownership of fraud strategy for a lending product (preferred).
Working knowledge of the full risk-control stack including fraud detection, underwriting, loss forecasting, and dispute/collection management.
Experience in ML-based detection and statistical modeling, including scorecard development, anomaly detection, clustering, feature engineering, and model monitoring.
Proficiency in SQL and Python with experience in Databricks, Hive, Hadoop, and other modern data platforms.
Strong communication skills to articulate complex fraud strategy trade-offs to senior leadership and non-technical stakeholders.
Experience with third-party fraud and identity vendors.