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

About the role

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.

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, LightGBM, 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.

Benefits

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

AI-First Engineering Culture
90%+ of code is AI-assisted. Engineers focus on architecture and complex problems.

Direct Access to the AI Frontier
Connect with leading AI companies in Silicon Valley, gaining first-hand exposure to cutting-edge advancements.

Pay

Competitive salary and benefits package.

Schedule

Full-time position.