Senior​/Lead Risk Analyst, Payment fraud

Position: Senior/Lead Risk Analyst, Payment fraud (Relocation to Toronto Required)

Lead Risk Analyst, Payment fraud

At Snaplii, risk management isn't a "brake" on growth-it's the "supercharger" that enables our 300% explosive expansion. We aren't looking for analysts who just read reports; we want strategists who can reverse-engineer fraud loops and command AI to automatically sever risks.

Important:

Location & Relocation

This role is based in our Toronto, Canada office. To foster our high-velocity founding culture, we require this leader to be onsite in Toronto for at least the first 6-12 months. We provide full relocation assistance and comprehensive immigration sponsorship for qualified US-based candidates.

About Snaplii

Snaplii is one of Canada's fastest-growing fintech platforms, with $100M+ in annual transaction volume and 350,000+ users across North America, Snaplii connects consumers with 500+ leading brands across everyday categories - enabling smarter spending with instant savings and rewards.

Today, Snaplii is evolving beyond a digital wallet into infrastructure for AI-native commerce - enabling secure, programmable transactions between users, brands, and AI agents.

AI drives demand. Snaplii executes the transaction.

About the Role

We are looking for a Risk Leader with deep fraud expertise and raw analytical horsepower. This role will shape and implement cutting-edge risk strategies that drive sustainable growth, minimize losses, and enhance the customer experience. The ideal candidate has direct experience in payment fraud detection and prevention, with the ability to spot fraudulent transactions and translate fraud patterns into scalable, data-driven solutions.

This role requires a balance of hands-on fraud investigation, SQL-driven analytics, and collaboration with product and engineering teams to design and implement automated fraud controls.

Key Responsibilities

• Lead the end-to-end development and execution of financial risk strategies-from opportunity identification to design, testing, launch, and post-production performance monitoring.
• Identify, investigate and monitor fraudulent or anomalous activity, including isolating and quantifying specific trends driving changes to fraud and payment patterns.
• Analyze internal and external data and produce authoritative reports and root-cause analysis on fraudulent activities and chargebacks.
• Experienced in collaborating with engineers and product managers to successfully deploy fraud prevention solutions that balance growth with risk control.
• Act as a liaison between the company and payment processors/vendors, with strong communication skills to speak the industry language, manage vendor relationships, and ensure effective alignment on fraud and risk management.

Qualifications

• The ideal candidate is an accountable and resilient team-player who brings a combination of business instincts, technical skills and raw analytical horsepower necessary to support the rapid growth of Snaplli's business.
• Minimum 5 years of professional work experience;

Minimum 3 years in a fraud-related role;

Minimum 1 year in the payments industry.

• Experience working with various payment methods in multi-currency environments, ideally within e-commerce or related industries.
• Proven ability to investigate and identify fraudulent activities, including hands-on experience with transaction reviews and fraud case analysis.
• Strong data modeling skills (3+ years): hands-on experience building fraud detection models, user behaviour scoring systems, and transaction anomaly detection models, including feature engineering, model training, evaluation, and deployment.
• Ability to integrate models with risk systems to enable automated, model-driven fraud prevention workflows.
• Proficiency with machine learning frameworks (Python or R with Sklearn, XGBoost, Light

GBM, etc.) and prior experience deploying models into production environments.

• SQL proficiency (must-have) - able to independently query and analyze large datasets. Python (good-to-have).
• Previous experience as a Fraud Analyst, Risk Analyst, Operations Specialist, Data Scientist, or Product Manager. Bachelor's degree in Engineering, Computer Science, Statistics, Finance, or a related analytical/technical field.
• Strong problem-solving skills and reverse-engineering thinking, with the ability to anticipate and predict potential risks from a fraudster's perspective.
• Proficiency in Mandarin Chinese is an asset but not required.

Why Join Us

• Building AI-Native Payments
• Powering how AI agents transact in the real world.
• Explosive Growth
• 300%+ revenue & TPV growth in 2025, with accelerating momentum into 2026.
• Small Team, Massive Scale