Senior Staff Fraud & Risk Analyst

Intuit; Mountain View (CA); Sur place; USD 200 000 - 270 000; Plein temps

Résumé du poste

Intuit is seeking a Senior Staff Fraud & Risk Analyst to own end-to-end lending fraud strategy across TL, LOC, and RBF, shaping policy from onboarding to recovery.

You will collaborate with Product, Engineering, Data Science, Finance, Legal, and Compliance to design detection systems, improve risk decisions, and scale AI-driven automation while balancing revenue and customer experience.

Qualifications

• MS/PhD in quantitative field with 7+ years in fraud analytics or data science.
• Fintech / lending experience strongly preferred.
• Proficient in SQL and Python; comfortable with Databricks, Hive, Hadoop.
• Experience with ML-based detection and scorecard development.

Responsabilités

• Own end-to-end lending fraud strategy across Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF).
• Design and tune detection systems for stolen/synthetic identity (FRAPP), account takeover (ATO), first-party fraud, and bust-out rings.
• Lead investigations of multi-product fraud rings and translate learnings into permanent policy.
• Partner with Data Science on ML detection models, risk-based decisioning, and AI automation for case review and policy execution.
• Set risk strategy for new lending initiatives—balancing revenue enablement against fraud loss and customer experience.
• Partner with Finance on loss forecasting, loss reserves, and unit economics to ensure risk-informed lending decisions.
• Partner with Risk Operations on case review workflows and fraud-hold enforcement at money movement.
• Partner with Legal/LCPO and Compliance on regulatory risk—Reg B/Z, ECOA, UDAAP, adverse action, KYB/KYC, sanctions.
• Champion AI and automation—identify opportunities to apply AI-powered tooling and automation to accelerate risk strategy work.
• Communicate complex fraud strategy decisions clearly to senior leadership, financial partners, and regulators.

Connaissances

SQL

Python

ML modeling

Data visualization

Cross-functional

Communication

Regulatory risk

Fraud analytics

Leadership

Problem solving

Formation

MS/PhD in quantitative field

Outils

Databricks

Hive

Hadoop

Description du poste

Overview

Intuit QuickBooks is leading the charge in revolutionizing financial services for small and mid-market businesses through its money movement platform. Lending — Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — is one of the fastest-growing pillars of that platform, extending credit to millions of small businesses that traditional lenders overlook. Growing responsibly at that scale requires world-class fraud strategy.

The Lending Fraud Policy team combines cutting-edge AI, cross-product signal intelligence, and deep credit-risk expertise to detect and prevent fraud across the full lending lifecycle — from onboarding, through underwriting, disbursement, servicing, and recovery — while enabling frictionless experiences for good customers. As a Senior Staff Fraud & Risk Analyst, you will own end-to-end lending fraud strategy across Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — the policy, detection systems, and operational governance that determine who Intuit lends to, on what terms, and how we defend the book from stolen/synthetic-identity fraud (FRAPP), account takeover (ATO), first-party fraud, and bust-out rings. You will collaborate with Product, Engineering, Data Science, Finance, Legal/LCPO, Risk Operations, and Internal Audit to design and continuously improve the risk experience.

If you are passionate about solving real customer problems through decision science and analytics — and building the fraud strategy that lets Intuit safely extend credit to businesses no one else will — we welcome you to join our talented team.

Responsibilities

• Own end-to-end lending fraud strategy across Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — set policy across the full lifecycle: onboarding eligibility, application underwriting, line/loan issuance, draw-level risk assessment (LOC and RBF), funding speed etc.
• Design and continuously tune detection systems for stolen/synthetic identity (FRAPP), account takeover (ATO), first-party fraud, and bust-out rings across all lending products.
• Lead complex forensic investigations of sophisticated multi-product lending fraud rings; drive same-day containment and translate ring learnings into permanent policy.
• Partner with Data Science on ML detection models, risk-based decisioning, and AI Agent automation for case review and policy execution.
• Set risk strategy for new lending initiatives — including NTTF (New-to-the-Franchise) Line of Credit (LOC) — balancing revenue enablement against fraud loss and customer experience;
• Partner with Finance on loss forecasting, loss reserves, and unit economics to ensure lending decisions are both risk-informed and revenue-aware.
• Partner with Risk Operations on case review workflows, fraud-hold enforcement at money-movement, and portfolio cleanup after ring detection.
• Partner with Legal/LCPO and Compliance on regulatory risk — Reg B/Z, ECOA, UDAAP, adverse action, KYB/KYC, sanctions (OFAC).
• Champion AI and automation — identify opportunities to apply AI-powered tooling and automation to accelerate risk strategy work, reduce manual toil, and improve decision quality and consistency at scale.
• Communicate complex fraud strategy decisions clearly to senior leadership, financial partners, and regulators.

Qualifications

• MS/PhD in a quantitative field (Statistics, Mathematics, Economics, Operations Research, Finance, or related) with 7+ years in fraud analytics, credit risk, or data science
• Fintech / Lending experience strongly preferred — hands-on ownership of fraud strategy for a lending product
• Working knowledge of the full risk-control stack — fraud detection, underwriting, loss forecasting, disputes/collections and loss reserves.
• Demonstrated experience in ML-based detection and statistical modeling — scorecard development, anomaly detection, clustering, feature engineering, model monitoring.
• Proficient in SQL and Python and comfortable working in Databricks, Hive, Hadoop, and modern data platforms; strong data visualization skills.
• Active champion for AI and automation adoption — you seek opportunities to apply AI-powered tooling and automation to risk strategy work.
• Polished partnership and communication skills — able to interact cross-functionally with business and technical partners; articulate complex fraud strategy trade-offs to senior leadership and non-technical stakeholders; influence decision makers, negotiate win-win outcomes, and drive prioritization across teams.
• Demonstrated ability to manage complex, cross-functional projects and deliver in a fast-paced, ambiguous environment; responsive during high-pressure incidents.
• Experience with third-party fraud/identity vendors

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

The Expected Base Pay Range For This Position Is

Mountain View $199,500 - $270,000