Staff Data Scientist - Risk
New
Work from anywhere in CanadaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSQLMachine LearningSnowflakeData scienceRisk Managementdbt
Requirements
- 8+ years of experience in data science, decision science, analytics, or related quantitative roles.
- Strong expertise in fraud analytics, trust & safety, AML, compliance analytics, or financial risk management environments.
- Advanced proficiency in SQL and Python for large-scale analytical workflows and data modeling.
- Experience applying statistical modeling and machine learning techniques such as regression, classification, experimentation, or causal inference.
- Strong understanding of modern cloud-based data ecosystems and analytical tools such as Snowflake, dbt, or similar platforms.
- Proven ability to work effectively in fast-paced, ambiguous environments with high ownership and evolving priorities.
- Excellent communication and stakeholder management skills, with the ability to present complex insights clearly to technical and non-technical audiences.
- Demonstrated ability to influence cross-functional teams and align analytical initiatives with broader business goals.
- Interest or experience within fintech, payments, cryptocurrency, or regulated industries is highly valued.
- Strong problem-solving mindset, business judgment, and ability to balance customer experience with operational risk management.
Responsibilities
- Lead the design and evolution of analytical frameworks, risk metrics, and decision-making models to support fraud prevention and operational efficiency.
- Partner cross-functionally with engineering, compliance, security, operations, and product teams to define scalable risk systems and improve customer protection strategies.
- Analyze fraud patterns, scams, abuse trends, and operational inefficiencies across financial and digital product ecosystems.
- Build dashboards, reporting frameworks, and monitoring systems to track fraud loss, false positives, customer friction, and control effectiveness.
- Evaluate and optimize risk controls through statistical analysis, experimentation, and selective machine learning applications.
- Conduct deep-dive investigations into incidents and emerging threats, translating findings into actionable business recommendations.
- Establish analytical standards, data quality practices, and scalable processes that improve decision-making across the risk organization.
- Influence long-term roadmap planning by identifying opportunities to improve automation, tooling, and operational workflows.
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