Job DescriptionWhat's the opportunity?You will help lead a team of Data Scientists and Fraud Analysts in applying advanced methodologies to develop effective and balanced fraud detection and client treatment strategies across products and channels. You will work with Fraud Data Science & Analytics (DSA), Fraud Strategy, Operations and IT teams, to optimize and balance performance across the fraud pillars - fraud loss, operating cost, client experience and risk framework. You will improve our ability to leverage technologies and data streams to enhance real-time decisioning capabilities. You will ensure effective fraud prevention and detection rules across several decision engines, and appropriate resourcing across product squads.
What will you do?- Lead Data Scientists and Fraud Analysts in developing an effective and balanced fraud prevention and detection program through a best-in-class suite of fraud rules and predictive models
- Develop and implement standards, routines and monitoring to improve overall efficiency and effectiveness of the team,
- Explore new analytic solutions, technologies and methodologies to enhance the bank's position as a global leader in the fraud industry
- Collaborate effectively with partners in Fraud IT on the continuous improvement of the fraud decisioning ecosystem
- Maintain a strong/collaborative relationship with Fraud Strategy and Operations in product squads, focused on highlighting emerging fraud risks and developing cross-discipline approaches to mitigate fraud
- Ensure all functions (applications and procedures) within the team are carried out in a risk conscious manner, and key audit controls and information security controls are consistently satisfied
- As an expert in data and fraud detection, support Fraud Management's strategic priorities and various analytic exercises and data extractions
What you need to succeedMust have:- 5+ years of experience in data science, machine learning, data mining or statistics
- Degree in Mathematics, Statistics, Computer Science or other related field
- Ability to perform complex data analysis on large volumes of data and present findings to stakeholders
- Strong practical knowledge of, and proven experience with, analytical software packages and various programming languages (eg: SAS, R, SQL, Python etc.)
- Demonstrated experience working with big data technologies (Hadoop, Hive, Apache Spark etc)
- Exceptional communication and presentation skills, including the ability to effectively communicate analytical recommendations to both technical and non-technical audiences
- Demonstrated leadership capabilities and proven ability to lead data analytics teams
- Strong critical thinking and analytical skills with the ability to solve complex problems and develop data-driven solutions
Nice to have:- Graduate degree in Mathematics, Statistics, Computer Science or other related areas
- Knowledge of Canadian banking and payment industry payments transaction data and financial fraud
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 and opportunities
- Ability to make a difference and lasting impact.
Job SkillsArtificial Intelligence (AI), Big Data Management, Data Mining, Data Science, Decision Making, Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language), Statistical Analysis
Additional Job DetailsAddress:YORK MILLS CENTRE, 36 YORK MILLS RD:TORONTO
City:Toronto
Country:Canada
Work hours/week:37.5
Employment Type:Full time
Platform:PERSONAL & COMMERCIAL BANKING
Job Type:Regular
Pay Type:Salaried
Posted Date:2026-08-14
Application Deadline:2026-08-28
Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above
