Role Description
Our client is one of Canada's fastest-growing fintech platforms, transforming how consumers pay, save, and earn rewards. Founded in 2021, the company has scaled to over $100M USD in annual transaction volume and 200,000+ users across North America, supported by a proprietary, fully in-house technology stack. The platform partners with 500+ leading brands and recently expanded into the US market, with teams in Etobicoke, ON and San Jose, CA. The company is targeting 4x year-over-year GMV growth and is actively building out its financial product suite.
We are recruiting a Senior/Lead Risk Analyst, Payment Fraud on behalf of our client. This is a strategic, hands-on role sitting at the intersection of fraud intelligence, data analytics, and product partnership. The successful candidate will be the primary fraud risk owner — responsible for designing and executing end-to-end fraud prevention strategies, investigating anomalous activity, and collaborating closely with engineering and product teams to build automated fraud controls. This role is ideal for a self-driven analyst who can operate independently, speak the language of payment networks, and translate fraud patterns into scalable, data-driven solutions.
Key Responsibilities
Lead the full lifecycle of financial risk strategy: opportunity identification, design, testing, launch, and ongoing performance monitoring.
Investigate and monitor fraudulent or anomalous transaction activity; isolate and quantify trends driving changes in fraud and chargeback patterns.
Analyze internal and external data to produce authoritative root-cause reports on fraudulent activities and payment anomalies.
Partner with engineering and product teams to design and deploy automated fraud prevention solutions that balance growth with risk control.
Serve as the primary liaison between the company and external payment processors and fraud vendors (e.g., Stripe, Visa, Mastercard); manage vendor relationships and ensure alignment on fraud and risk management.
Build and maintain fraud detection models, user behaviour scoring systems, and transaction anomaly detection workflows.
Integrate models into risk systems to enable automated, model-driven fraud prevention at scale.
Requirements
Minimum 5 years of professional experience, including at least 3 years in a fraud-related role and at least 1 year in the payments industry.
Hands-on experience with payment fraud — specifically within card-based payment networks (chargebacks, 3DS, Visa/Mastercard dispute workflows). Credit risk experience alone is not sufficient.
Proven experience investigating fraudulent activities, including transaction reviews and fraud case analysis in multi-currency, e-commerce, or fintech environments.
Strong data modelling skills (3+ years): feature engineering, model training, evaluation, and production deployment of fraud detection and anomaly detection models.
Proficiency with ML frameworks: Python or R with Sklearn, XGBoost, LightGBM, or equivalent.
SQL proficiency (must-have) — able to independently query and analyze large datasets.
Experience working with third-party fraud vendors and payment processors; ability to communicate fluently in industry terminology.
Strong problem-solving instincts — ability to think like a fraudster and anticipate emerging attack vectors.
Bachelor's degree in Engineering, Computer Science, Statistics, Finance, or a related analytical field.
Conversational Mandarin (Cantonese also acceptable) is strongly preferred; candidates with Chinese-Canadian background encouraged to apply.
We Offer
Competitive salary of CAD $150,000 – $200,000 based on experience.
Comprehensive health benefits.
Performance bonus.
A high-impact, solo-ownership role with direct influence over the company's risk infrastructure.
Fast-paced startup environment with strong growth trajectory — $100M+ GMV and targeting 4x year-over-year growth.
If you are a payment fraud specialist who thrives in a high-ownership environment and wants to build a fraud prevention program from the ground up, we encourage you to apply.
