Manager, AML/ATF Screening Models and Analytics, Global AML

Scotiabank•Toronto, ON

4d•Onsite

About The Position

The Manager, AML/ATF Models and Analytics is responsible for applying sophisticated predictive/analytical modelling techniques and latest AML/ATF rules and scenarios to new models AND for enhancing the performance of existing Client and Payment Screening models. This role contributes to the overall success of the Global Compliance and AML team by ensuring specific individual goals, plans, and initiatives are delivered in support of the team’s business strategies and objectives. Also ensures all activities conducted follow governing regulations and internal policies and procedures.

Requirements

Bachelor's degree or higher in Data Science, Statistics, Mathematics, Engineering, Computer Science, or another quantitative discipline.
Strong technical proficiency in SQL and Python, with hands-on experience extracting, transforming, and analyzing large, complex datasets to support advanced analytics and model development.
Experience designing, developing, testing, tuning, and implementing analytical, statistical, and/or machine learning models.
Ability to operate with a high degree of autonomy, independently leading complex analytical and modeling initiatives end-to-end while engaging appropriate stakeholders when required.
Strong analytical and critical thinking skills, with the ability to translate detailed data insights into meaningful business, risk, and regulatory outcomes.
Demonstrated ability to collaborate effectively across cross-functional teams, including technology, data, business, operations, and governance stakeholders.
Excellent written and verbal communication skills, with the ability to clearly communicate complex technical concepts to senior management, audit, and regulatory audiences.
Strong understanding of systems architecture, data flows, upstream/downstream dependencies, and interactions across multiple technology platforms.
Proven ability to manage multiple concurrent initiatives and competing priorities in a fast-paced environment.
Sound judgment and decision-making skills within a high-risk, highly regulated AML/ATF environment, balancing innovation, risk management, and governance expectations.

Nice To Haves

Experience working with AML/ATF, financial crime, screening, fraud, risk analytics, or other highly regulated domains is considered an asset.
Experience with AML/ATF screening platforms, model governance, validation, audit support, or regulatory compliance activities is considered an asset.
Experience developing dashboards and reporting solutions using tools such as Power BI is considered an asset.

Responsibilities

Lead data extraction, transformation, and advanced analysis using Python (pandas, numpy, ML libraries) and SQL to support scalable analytics and model development.
Design, develop, test, tune, and implement statistical and machine‑learning models, including AML/ATF client and payment screening rules and models.
Conduct in‑depth data profiling and analysis of client and payment source data to identify data quality gaps, enrichment opportunities, and predictive modeling enhancements.
Evaluate the effectiveness of screening systems and models, identifying risks, gaps, and performance issues, and provide clear, prioritized recommendations for improvement.
Identify and demonstrate measurable value in opportunities to improve model efficiency, effectiveness, and alert quality through analytical and modeling enhancements.
Translate complex analytical and modeling outputs into clear, actionable insights for both technical and non‑technical stakeholders.
Produce high‑quality model documentation to support model development, validation, governance, and audit requirements.
Properly identify, report, and escalate issues that may impact the AML/ATF program resulting from model performance, system functionality, data issues, or application changes.
Apply hands‑on experience with AML/ATF screening platforms to support model tuning, alert optimization, and performance analysis.
Develop analytical views, reports, or dashboards (e.g., Power BI) to communicate trends, model performance, and risk insights to senior stakeholders.