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Snaplii · 4 months ago

Senior/Lead Risk Analyst, Payment fraud

New York, NY

Full-time

Onsite

Senior Level

5+ years exp

Snaplii is one of Canada’s fastest-growing fintech platforms, and they are seeking a strategic and data-driven fraud and risk leader to join their Risk & Payments team. This role involves shaping and implementing risk strategies to minimize losses and enhance customer experience, while also collaborating with product and engineering teams to deploy fraud prevention solutions.

Financial ServicesFinTech

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

Qualification

Fraud detection experienceSQL proficiencyData modeling skillsMachine learning frameworksPayments industry experienceMandarin ChineseProblem-solving skillsCommunication skillsTeam player

Required

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

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