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Manager, Fraud Research and Analytics

2 months ago


Toronto, Ontario, Canada Royal Bank of Canada Full time

Job Title: Fraud Data Science & Analytics Specialist

Company: RBC

Job Summary: Identify patterns and trends in fraud data to advise on emerging trends and systemic issues, and develop predictive analytics to forecast financials and fraud KPIs.

Fraud Management teams employ deep subject matter expertise in identifying, assessing, mitigating and controlling fraud risks for the Enterprise. Within Fraud Management, the Fraud Data Science & Analytics (FDSA) team develops and optimizes fraud detection strategies to maximize operational effectiveness and minimize client impact. The FDSA Research and Analytics function conducts analytic research into fraud issues, identifying causation and correlation, develop insights on emerging trends, gaps or systemic issues, and collaborate with Fraud Management partners to communicate results and take action.

Key Responsibilities:

  • Identify patterns, trends and hidden causation and correlation relationships from Fraud data and advise FM SMT of emerging trends, specific gaps or systemic issues that impact the Fraud Risk of RBC
  • Collaborate with appropriate partners for action, correction, awareness and training
  • Forecast Financials and Fraud KPI's using predictive analytics techniques and generate insights for executive decision making and communicating results through insightful visualizations, white papers, reports and presentations
  • Research business problems, develop solutions to support investigation and analysis and lead progression of analytics methodology, tools and measurement to create development templates for automation
  • Build the foundation of information in consolidated data repositories for Operations, Fraud Events, Fraud Performance and Financials
  • Provide a complete picture of the fraud landscape with drill-down capabilities for root cause analysis and deliver self-serve interactive visualizations through accessible platforms like Tableau
  • Conduct Code Reviews and develop standards for Development practices and partner with project teams to support information required for analysis of the current state
  • Deliver Project Reporting for Fraud Events, Performance, Operational and Financial KPI's and KRI's and Program Benefits measurements to track success criteria of the Project Business Case

Requirements:

  • 2+ years' experience in a Data science and analytics
  • Advanced capability in using Python, R, PySpark, SAS, SQL and Tableau
  • Experience with Databases and Data Structures (RDBMS, Hadoop etc.)
  • Ability to apply data and analytics concepts to business problems and explain business value of data and analytics concepts
  • Ability to multi-task and prioritize and complete projects effectively and in a timely manner
  • Strong team player with a track record of collaboration and experience working as part of an agile team
  • Professional verbal and written communication and presentation skills, and ability to provide consultation, analysis, and recommendations on a broad range of projects in a deadline driven environment with ambiguity

Nice-to-have:

  • Good understanding of Canadian Financial Institutions, with a focus on Fraud/Risk Operations
  • Degree in mathematics, statistics, economics, computer science, software engineering or related quantitative field

What's in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

  • Fair compensation
  • Challenging work in a fast-paced timeline driven work environment
  • Ability to make a difference and lasting impact
  • Work with a great and supportive team

Job Skills:

Base SAS, Big Data Management, Data Science, Decision Making, Hadoop Distributed File System (HDFS), Hadoop Ecosystem, Machine Learning, Predictive Analytics, PySpark, Python (Programming Language), Relational Database Development, R Programming, SAS Enterprise Guide, Statistical Analysis, Structured Query Language (SQL), Tableau (Software)