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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
- Business Data Analyst partnering with stakeholders to shape product decisions through experimentation and analysis. Driving measurable user experience outcomes within the Revenue Operations team in a hybrid role.
Responsibilities
- Collaborate closely with Product Owners and business stakeholders to design and plan experiments, define requirements, assumptions, and limitations
- Explore and analyze experiment data to understand customer behaviour, usage patterns, and performance drivers
- Propose, design, and evaluate experiments and causal analyses to answer well-scoped business questions
- Create new projects and manage multiple analytical initiatives informed by experiment results and emerging insights
- Conduct deep dive analyses to uncover trends, friction points, and opportunities
- Build dashboards and automated reports to improve accessibility and insight quality
- Translate business questions into measurable metrics
- Deliver data based recommendations and clearly identify actionable next steps
- Improve tracking plans, event quality, and attribution logic to ensure accurate and reliable measurement
- Write and optimize SQL queries and notebooks to support analysis
- Use AI assisted tools to support data exploration, query development, and analytical workflows
- Communicate data analysis results, insights, and recommendations clearly to stakeholders
- Collaborate with fellow analysts to review results, refine methodologies, and strengthen analytical approaches
Requirements
- Post-secondary education in Business, Economics, Mathematics, Data Science, Computer Science, or other related programs
- Experience in data analysis or product analysis
- Strong communication skills and ability to explain complex analysis clearly
- Solid understanding of statistics and experiment analysis
- Proficiency in SQL, including writing efficient queries with joins, aggregations, and filtering logic
- Knowledge of data mining and segmentation techniques
- Familiarity with Python for data analysis and visualization (ex. pandas, Matplotlib)
- Ability to manage multiple experiments and analyses simultaneously
- Experience analyzing and designing A/B experiments
- Proficiency using Microsoft Excel (Pivot Tables, Visualizations, Formulas) and Project Management software
- Ability to work independently and manage complex projects
- Strong interest in data analysis and a natural tendency to dig deeper into unexpected results.
Benefits
- Hybrid work arrangement
- Opportunities for a collaborative and inclusive learning culture
- Fast, easy to use, and professional solutions
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