Drimva

JPMorgan Chase

Core Cash Fraud Prevention Product Manager - Payments - Vice President

$100K–$180K

Chicago, IL +1 · Director · Full-time · Posted Jul 10, 2026

Location
Chicago, IL
Tampa, FL
Experience
5+ years or equivalent expertise in product management or a relevant domain; minimum 3 years of Financial Technology experience. Preferred: 6-12 years in software development or technology companies.
Education
Bachelor's or Master's degree required; Engineering, Computer Science, Mathematics, or related quantitative field.

About the job

Leads product strategy and delivery for a global fraud-data platform and fraud-prevention solutions across core cash payment channels.

What you'll do

  • Direct the design, development, and implementation of the Global Fraud Consortium platform and its business rules.
  • Set product vision and roadmap, manage the backlog, and track success metrics including fraud effectiveness and false positives.
  • Analyze fraud patterns and work with data science, compliance, legal, and risk teams on detection models and regulatory requirements.

What they ask for

  • 5+ years of product management or relevant domain experience, or equivalent expertise; minimum 3 years of Financial Technology experience.
  • Deep knowledge of banking payments, fraud detection systems, and payment fraud patterns and workflows.
  • Experience designing and implementing payment and fraud-prevention solutions; proficiency in Agile methodologies and JIRA.
  • Bachelor's or Master's degree required; experience with payment regulations, API specifications, SOAP, XML, JSON, SQL, and database scripting.

Nice to have

  • Experience in a highly matrixed, complex organization.
  • Expertise in fraud-detection methodologies, machine-learning models, rule-based engines, or scoring models.

Pay

USD $114,000.00-$180,000.00 base salary; USD $99,750.00-$170,000.00 base salary

Tools

JIRA, Swagger, SOAP, XML, JSON, SQL, ISO 20022

AI focus

Collaborates with data science teams to develop and deploy machine-learning models for fraud detection.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 6, 2026 (UTC)