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Fraud & AML Strategy Co-op (BS / MS Students) - Canada at DataVisor. About DataVisor. DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.. Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!. Co-op Role Overview. We are looking for curious, analytical, and motivated BS or MS co-op students who are interested in fraud prevention, AML, risk analytics, and strategy design. This role is ideal for students who enjoy problem-solving, asking “why” and “what if,” and turning data into actionable insights.. As a Fraud & AML Strategy Co-op, you will work closely with experienced fraud strategists, data scientists, and product teams to help design, test, and optimize fraud and AML detection strategies across payments, account onboarding, and money movement use cases.. This is a learning-focused role with hands-on exposure to real-world fraud patterns, analytics tools, and decision workflows used by global enterprises.. What You’ll Do. Fraud & AML Rule Development. Assist in designing, testing, and tuning fraud and AML detection rules across account origination, transactions, and suspicious activity monitoring. Help maintain and document rule libraries with proper governance and performance tracking. Analytics & Experimentation. Analyze transaction data to identify trends, anomalies, and emerging fraud or AML typologies. Support experiments and pilot initiatives to test new fraud signals or detection approaches. Cross-Functional Collaboration. Partner with Data Science teams to translate fraud patterns into model features. Work with Fraud & AML Operations teams to review case outcomes and validate detection coverage. Workflow & Process Support. Assist in defining fraud and AML workflows, including alerts, queues, and escalation paths. Help evaluate third-party fraud or AML data sources for enrichment opportunities. Insights & Reporting. Contribute to dashboards, reports, and presentations that summarize fraud trends and strategy performance. Support strategy recommendations for internal stakeholders. Currently enrolled in co-op program or recently completed a Bachelor’s or Master’s degree in:. Computer Science, Data Science, Engineering, Economics, Business Analytics, Finance, or a related field. Curious about fraud prevention, AML, risk management, and fintech. Comfortable working with data and eager to learn industry tools and frameworks. Able to think analytically and communicate findings clearly. Canadian Citizen or PR of Canada. Basic Qualifications. Experience with or coursework in SQL (required). Familiarity with Python, data analysis, or statistics through coursework or projects. Strong analytical and problem-solving skills. Ability to work independently and collaboratively in a fast-paced environment. Nice to Have (Not Required). Exposure to fraud, AML, payments, or financial services concepts. Experience with dashboards or analytics tools (e.g., Tableau, Looker, Power BI). Understanding of rule-based systems, workflows, or case management concepts. Company Location: Canada.