City : Toronto

Category : Technology | Analytics | Research

Industry : Financial/Banking

Employer : RBC

What's the opportunity?

Within Fraud Management, the Fraud Data Science & Analytics (FDSA) team is responsible for developing and optimizing fraud detection strategies to maximize operational effectiveness and minimize client impacts. FDSA is also responsible for analytics in support of planning and decision management, contributing meaningful insights and informing sound business decisions.  In this role, you will cycle through 4 FDSA teams, spending three months in each of the functions listed below, before settling within one of the teams.

Quantitative Analytics and Monitoring - identify causation/correlation, develop insights on emerging trends, gaps or systemic issues

Detection and Optimization - improve detection, efficiency, automate and streamline processes

Data Science and Innovation - develop predictive models for improving fraud detection capabilities, optimize productivity tools, create automated workflows to replace manual processes, design forward thinking and innovative solutions to complex problems, etc.

Opportunity to express interest in spending three months in one of the following three areas:

Value, Assurance and Insights - develop fraud risk models and features (Data Solutions)

Data Engineering - create data pipelines to transform raw data into useable datasets

Data Strategy - contribute to the Fraud technology strategy

What will you do?

• Identify patterns/trends, uncover hidden relationships in fraud data and proactively inform and advise FM SMT of emerging trends, gaps or systemic issues that impact the fraud risk to RBC
• Forecast Financials and Fraud KPI’s using sophisticated predictive analytics techniques, research business problems, develop solutions to support investigation and analysis
• Develop fraud detection strategies for channels & products, explore data sources to identify emerging fraud trends and apply advanced analytical techniques to develop new fraud detection strategies
• Monitor the performance of existing fraud detection strategies and optimize these strategies, leveraging analytical tools to maintain and improve detection strategies
• Prioritize fraud detection alerts across all detection platforms, verify accuracy of rule deployments through testing and balance between fraud losses, client experience and operational costs
• Develop supervised machine learning (ML) models for real-time fraud detection and explore opportunities to apply ML solutions to other challenges within Fraud Management
• Design and build automated reporting with appropriate metrics for DSA’s risk scoring models and establish appropriate thresholds for automated alerts on model performance
• Create data pipelines that transform raw data into useable datasets, working with data in HDFS, object storage services (i.e. S3), ElasticSearch, and develop product recommendations

What do you need to succeed?

Must-have

• Data analytics experience, including related project experience from school
• Advanced knowledge of, and proven experience with using SQL, Python and PySpark  
• Working knowledge of RDBMS and Hadoop
• Ability to apply data and analytics concepts to business problems and explain these concepts to non-technical audiences
• Ability to understand and explain business value of data and analytics concepts
• Strong team player with a proven track record of collaborating within a team and ability to multi-task and prioritize/complete projects in a timely manner
• Professional oral and written, communication and presentation skills
• Proven analytics and problem-solving skills, and ability to partner and interact with clients, colleagues, and the wider community

Nice-to-have

• Experience in Machine Learning, data mining and statistics; ability to perform complex data analysis on large volumes of data and present findings to stakeholders
• Good understanding of Canadian Financial Institutions, with a focus on Fraud/Risk Operations
• Experienced in using SAS and R
• Bachelor’s 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.

• A comprehensive Total Rewards Program
• Leaders who support your development
• Ability to make a difference and lasting impact
• Opportunity to take on progressively greater accountabilities

Job Skills

Artificial Intelligence (AI), Big Data Management, Data Mining, Data Science, Decision Making, Machine Learning, Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language), Statistical Analysis

Additional Job Details

YORK MILLS CENTRE, 36 YORK MILLS RD:TORONTOTORONTOCanada37.5Full timePERSONAL & COMMERCIAL BANKINGRegularSalaried2025-05-082025-05-23

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Inclusion and Equal Opportunity Employment

At RBC, we believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.