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Fraud Data Analyst
LinkedIn Recruiter Post
Hiring a Fraud Data Analyst for a 12 - month contract in Toronto, ON. Requires 5+ years in data/analytics with fraud, AML, and threat detection experience.
About the role
- Senior Analyst responsible for delivering analytics and insights in Customer Contact Center operations. Collaborates with stakeholders to ensure analytical outputs are actionable and measurable.
Responsibilities
- Deliver analytics and insights for Contact Center performance (SLA, volumes, handle time, repeat contact, channel mix).
- Build and maintain executive-ready Power BI dashboards and scorecards focused on clarity and decision-making.
- Conduct root-cause analysis on call/chat drivers using structured and transcript data.
- Partner with Contact Center leadership and Workforce Management on planning, performance, and operational decisions.
- Support Care Pod initiatives (chatbots, routing, agent assist) with experimentation, rollout measurement, and impact analysis.
- Translate prototypes (e.g., Figma) into production-grade analytics solutions, including data modeling and DAX optimization.
- Act as an AI analytics partner, defining KPIs, monitoring adoption, and embedding insights into workflows.
- Support analytics across acquisition, onboarding, and lifecycle marketing.
- Design and analyze A/B and multivariate experiments.
- Build customer cohorts to evaluate campaign performance, retention, and engagement.
- Develop dashboards and insights to track marketing performance and identify growth opportunities.
- Translate findings into actionable recommendations for targeting, channel strategy, and prioritization.
Requirements
- 3–5 years in analytics, BI, or data-driven roles (financial services is an asset).
- Strong SQL and Power BI (data modeling, DAX, dashboard design).
- Experience with experimentation (A/B testing) and cohort analysis.
- Proficiency in Python for statistical or analytical work.
- Ability to analyze structured and unstructured data (e.g., transcripts, NLP tools).
- Strong stakeholder management with ability to translate data into business insights.
- Comfortable working cross-functionally (Operations, Marketing, Product, Data Science).
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