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Punt

Fraud Prevention Analyst

Vancouver, BC, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 26, 2026
Posting language: English
Posted: Oct 27, 2026

Original job description

About the Role

Punt is entering an exciting new phase of growth, with new products and verticals launching alongside our existing business. With that growth comes new revenue streams, new fund flows, new systems, and plenty of interesting finance problems to solve.

We are seeking a detail-oriented Fraud Prevention Analyst to join our Risk team, based on-site at our Vancouver head office. This role is critical to protecting our online platform from fraudulent activity, minimizing financial losses, and ensuring a safe experience for our customers. This position requires close collaboration with our Customer Support, Product, and Engineering teams. The ideal candidate combines strong quantitative skills with 2–4 years of hands-on experience with fraud prevention, chargeback mitigation in electronic payments and data analysis.

What You'll Do

Monitor transactions in real time to identify, investigate, and mitigate fraudulent activity
Analyse chargeback and large datasets to detect emerging fraud patterns and trends
Develop, test, and refine fraud detection rules and models with moderate autonomy
Manage and resolve chargebacks, disputes, and payment reconciliation issues end-to-end
Collaborate closely with Customer Support, Product, and Engineering teams to improve fraud controls and customer experience
Prepare regular reports on fraud metrics, loss trends, and key risk indicators for leadership
Stay current on payment fraud trends, regulations, and industry best practices
Identify and help implement process improvements to reduce false positives and fraud losses
Mentor junior analysts or support onboarding as needed

What You Bring

Bachelor's degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field
Experience with fraud detection platforms or tools
2–4 years of experience working with electronic payments within North America, including chargeback processes and dispute resolution
Demonstrated experience analyzing and working with large datasets
Strong critical thinking and problem-solving skills, with the ability to work with moderate independence
Excellent attention to detail and sound judgment in ambiguous situations
Strong communication skills, with the ability to translate data insights into actionable recommendations for both technical and non-technical stakeholders
Must be able to work on-site at our Vancouver office
Legally entitled to work in Canada

Nice to Have

Familiarity with machine learning concepts applied to fraud/risk modelling
Prior experience in e-commerce specifically
Hands on experience with SQL, Python, R, or similar tools

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More about this job

Responsibilities

Monitor transactions, investigate and mitigate fraud, analyze chargebacks and large datasets, and develop and refine fraud detection rules and models. Resolve disputes and payment reconciliation issues, report on risk metrics, collaborate across teams to improve controls, and support process improvements and junior analyst onboarding.

Requirements

Requires a bachelor's degree in a quantitative field and 2–4 years of experience with North American electronic payments, chargebacks, and dispute resolution. Candidates should have experience with fraud detection tools and large datasets, strong analytical judgment and communication skills, and be legally entitled to work in Canada and able to work on-site in Vancouver.

Skills

Fraud Prevention
Fraud Detection
Chargeback Mitigation
Dispute Resolution
Electronic Payments
Data Analysis
Fraud Pattern Analysis
Fraud Detection Rules and Models
Payment Reconciliation
Risk Reporting
Critical Thinking
Problem-Solving
Communication
Machine Learning
SQL
Python

Visa sponsorship

Not detected in the job text

Keywords

Fraud Prevention
Fraud Detection
Electronic Payments
Chargebacks
Dispute Resolution
Payment Reconciliation
Transaction Monitoring
Fraud Patterns
Fraud Detection Rules
Fraud Models
Risk Analysis
Data Analysis
Large Datasets
Fraud Metrics
Loss Trends
Key Risk Indicators
False Positives
Customer Support
Product
Engineering
Machine Learning
Risk Modelling
E-Commerce
SQL
Python
R
North American Payments

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