Staff Product Manager — Real-Time Payment Risk & AI

Intuit; Mountain View (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Cash bonus

Equity rewards

Benefits program

Job summary

Intuit is seeking a Staff Product Manager to own bill payment and payroll transaction risk outcomes, driving loss reduction and reducing false positives. You will lead strategy, data foundation, and real-time decisioning across cross-functional teams including data science, engineering, risk policy, and operations.

You will partner with data vendors and internal data sources to optimize decision signals and counterparty risk features.

Qualifications

• 4+ years in Product Management with measurable business outcomes.
• Experience building risk/fraud decisioning strategies in payments or finance.
• Strong partnership with data science/ML teams and data vendors.

Responsibilities

• Own the strategy to achieve fiscal year goals for loss reduction and insult-rate reduction across bill payment and payroll transaction risk.
• Decide how models, rules, and signals combine to approve good transactions and stop bad ones in real time.
• Expand the data foundation by evaluating and integrating new data vendors and internal sources.
• Build counterparty risk signals to assess risk of transaction payees, vendors, employees, and contractors.
• Advance real-time, AI-native decisioning with latency, reliability, and explainability considerations.
• Own the scoreboard and communicate it with leaders, reporting loss/false-positive rates, approvals, and latency.

Skills

Product Management

Risk/Fraud decisioning

Cross-functional collaboration

Real-time decisioning

A/B testing

Education

Bachelor's degree

Master's degree or MBA preferred

Tools

APIs

Data pipelines

Job description

Intuit is seeking a Staff Product Manager to own bill payment and payroll transaction risk outcomes, driving loss reduction and reducing false positives. You will lead strategy, data foundation, and real-time decisioning across cross-functional teams including data science, engineering, risk policy, and operations.

You will partner with data vendors and internal data sources to optimize decision signals and counterparty risk features.