Staff Fraud Risk Analyst — AI-Driven Fraud Defense & Strategy

Intuit, Inc.; Mountain View (CA); On-site; USD 163,000 - 221,000; Full time

Benefits offered by this job

Competitive compensation

Bonus & equity

Job summary

Intuit, Inc. is seeking a Staff Fraud Risk Analyst to partner with product, engineering, data science, and investigations teams. You will protect Credit Karma perimeter and Marketplace by building intelligent fraud controls and guiding policy through data-driven insights.

You will mentor analysts and drive multi-quarter initiatives with a focus on scalable, measurable risk reduction. You will use advanced SQL and Python to analyze large datasets, develop dashboards, and communicate findings to

Qualifications

• 8+ years of relevant experience in fraud prevention, risk, or a similar analytical role, with strong business acumen.
• Advanced SQL and Python skills for profiling, segmentation, and aggregation on large datasets (BigQuery/Databricks preferred).
• Proven ability to find fraud patterns and risk in large datasets, and to use statistical analysis to justify policy recommendations.
• Experience in identity, authentication, or account-lifecycle fraud (ATO, account creation abuse, recovery abuse) and/or marketplace, lead, or partner fraud preferred.
• Hands-on experience applying AI/GenAI to fraud detection or analytics workflows.
• Experience with fraud KPIs (precision, recall, chargeback, customer insult rate) and with data visualization tools (Tableau, Looker, Excel, GSheets).
• Decisive judgment and ownership of outcomes in fast-moving cases, with a fraud and risk mindset and deep familiarity with the product surface being protected.
• Outstanding communication and influencing skills, with a record of driving decisions across org boundaries.
• Detail-oriented, proactive, accountable, team player, and empathetic.
• Bachelor’s degree in a quantitative field or equivalent experience; Master’s degree preferred.

Responsibilities

• Lead multi-quarter initiatives to understand fraudulent behavior across the Credit Karma perimeter and Marketplace, identify risk trends in the data, and turn them into policy and product recommendations.
• Become a subject matter expert for Credit Karma perimeter and Marketplace fraud, and own the fraud and risk case for launches, experiments, and roadmap tradeoffs.
• Make fast, well-reasoned calls on high-risk cases, and own the outcome through resolution, including post-incident learnings.
• Partner with product, engineering, data science, and investigations to build intelligent, scalable controls that balance fraud loss against member experience.
• Identify gaps in metrics and data, and own scalable fixes. Define and track KPIs such as fraud rate, precision and recall, customer insult rate, and ATO and fake-account rates. Build dashboards that show the impact of fraud and of the policies meant to stop it.
• Use AI and GenAI tools to speed up analysis, find new fraud patterns, and automate manual fraud workflows.
• Advocate for curated, standardized, centralized fraud datasets for Intuit-wide analysis.
• Use SQL, Python, Databricks, Tableau, and spreadsheets to analyze large datasets and guide policy decisions.
• Document and explain complex analyses and processes to leadership, and build alignment across technical and non-technical teams.
• Mentor and coach analysts, and raise the team’s technical and analytical bar.

Skills

Advanced SQL

Python

Fraud analytics

Data visualization

Education

Bachelor’s degree in quantitative field

Master’s degree preferred

Tools

BigQuery

Databricks

Tableau

Looker

Excel

GSheets

Job description

Intuit, Inc. is seeking a Staff Fraud Risk Analyst to partner with product, engineering, data science, and investigations teams. You will protect Credit Karma perimeter and Marketplace by building intelligent fraud controls and guiding policy through data-driven insights.

You will mentor analysts and drive multi-quarter initiatives with a focus on scalable, measurable risk reduction. You will use advanced SQL and Python to analyze large datasets, develop dashboards, and communicate findings to