Lead Data Scientist, Global AML Transaction Monitoring

As a Lead Data Scientist, Global AML Transaction Monitoring, you will develop and maintain efficient and effective AML/ATF Transaction Monitoring models that consider regulatory expectations, industry trends, and innovation opportunities.

You will be required to develop and tune new and existing solutions used to identify suspicious activity, analyze data to identify RBC clients who present an elevated level of risk (financial & reputational), and continuously look for opportunities to provide more relevant and valuable intelligence to the Global AML program.

Responsibilities

• Develop, tune, and maintain existing AML Transaction Monitoring machine learning, rule-based solutions
• Develop new AML Transaction Monitoring solutions using data science techniques
• Develop validation-ready model documentation and monitoring methodology for models, in adherence to Enterprise Model Risk Management Standards
• Create and document model performance metrics, and provide recommendations to the Director, Transaction Monitoring on any findings of model performance
• Collaborate with the Director, Transaction Monitoring to prioritize development of monitoring solutions
• Collaborate with other Monitoring & Analytics teams to identify and remediate RBC risk exposure, and implement / tune transaction monitoring solutions as necessary to address risk
• Collaborate with business partners and internal stakeholders to improve effectiveness and efficiency of transaction monitoring program
• Identify opportunities in applying new technologies to AML analytics
• Identifying industry best practices and new technologies for strategic consideration by the AML Monitoring and AML Governance Committee

Requirements

• 5+ years of experience conducting in-depth data analytics
• University or Master degree in an analytical field of study (e.g., Computer Science, Engineering, Mathematics, Statistics, or related quantitative field)
• Expertise in analyzing and manipulating large datasets with Python and Scala/Spark
• Proficiency and familiarity with different SQL, Hive databases, and GitHub
• Strong capability in exploratory data analysis and feature development
• Understanding of AML and Financial Crime Fundamentals
• Strong solution-oriented mindset, capable of synthesizing technical, operational, and business context into clear understanding of how models operate
• Exceptional attention to detail across documentation, testing artifacts, and quantitative analysis
• Strong written and verbal communication skills; capable of explaining model risk analytics findings to both technical and non-technical stakeholders, as well as tell stories with data meaningfully
• Understands project's needs and ability to translate these into detection models

Nice to Have

• Specific experience in Financial Crime or Transaction Monitoring Analytics roles
• Experience working in specific business or financial industry data domains (i.e., Capital Markets, Wealth Management)
• Experience with supervised and unsupervised learning, and NLP preferred

What's in it for you?

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
• Leaders who support your development through coaching and managing opportunities
• Ability to make a difference and lasting impact
• Work in a dynamic, collaborative, progressive, and high-performing team
• A world-class training program in financial services
• Opportunities to do challenging work. Opportunities to take on progressively greater accountabilities. Opportunities to build close relationships with clients
• Access to a variety of job opportunities across business and geographies