Resume

Global Head of Applied AI/ML in Corporate Technology (Compliance & Risk)

Lead a global data science and ML engineering organization through directors and managers, delivering AI end to end - from ideation and prioritization through development, evaluation and production deployment. Scope spans second-line-of-defense AML and compliance, with additional responsibilities across Legal and Resiliency.

• AI strategy & execution: Define multi-year AI/ML and generative AI roadmaps and the delivery operating model; partner with business, Product, Engineering and Risk leaders on use-case prioritization, funding and AI integration into enterprise platforms.
• Engineering transformation: Led implementation of specification-driven, AI-assisted SDLC practices across engineering teams.
• AML modernization: Lead the shift from scenario-based detection to relational intelligence using entity resolution and graph modeling.
• Agentic AI: Design multi-agent systems for investigation, review and documentation; deploy LLM-driven investigation agents and automate suspicious activity report (SAR) narratives, improving productivity and traceability.
• Legal AI: Deliver LLM and agentic capabilities for contract review, billing intelligence, litigation, regulatory compliance and eDiscovery.
• Operational resiliency: Implement agentic capabilities to optimize recovery plans, automate analysis and strengthen readiness.
• Responsible AI: Establish evaluation frameworks and LLM guardrails; embed human oversight, compliance validation and risk KPIs into deployment.

Head of Customer Intelligence & Optimization - Executive Director of Data Science

Built US Wealth Management's data science team from the ground up, leading managers and hiring, developing and retaining onshore and offshore talent across personalization, experimentation and customer and advisor intelligence.

• Product & personalization: Owned the Customer and Advisor Recommendation Engine (CARE) vision and roadmap; embedded recommendations in omnichannel experiences and led customer-360 delivery around life moments and triggers.
• Growth & customer intelligence: Led modeling and analytics for Wealth Plan's go-to-market strategy and customer journeys, alongside acquisition, retention and relationship-deepening analytics; applied ML to optimize advisor sales performance.
• Measurement & experimentation: Translated business OKRs into metrics and experiments; led test analysis, learning agendas and deep dives across owned and paid media to evaluate marketing effectiveness and inform investment.
• Predictive models: Led development of multi-touch attribution, digital acquisition-propensity and net present value (NPV) models for self-directed and advised investment accounts.
• NLP & LLMs: Led NLP and LLM-based topic modeling on financial content and implemented a chatbot for advisors and bankers.
• Executive decision support: Secured sponsorship and funding through ROI-based business cases; presented analytics in CEO business reviews and partnered with the CFO, CMO and GMs to align investment with business priorities.
• Data foundations & governance: Partnered with Technology and Risk on cloud architecture and scalable analytics across clickstream, customer and third-party data, enabling experimentation and automation. Established analytics quality processes and partnered with Model Risk Governance and Review (MRGR) on approvals from R&D through deployment, maintaining data privacy and regulatory controls.

Marketing analysis covered owned channels and paid media, including television, online video, affiliates and SEO. Data sources included credit, checking and business-operations transactions alongside customer and digital data. Manager coaching and analytics-quality practices supported team development.

Tech Stack: AWS, Snowflake, Spark, Airflow, Kafka, Hadoop/Cloudera/Hive, Cassandra, Teradata, Adobe Analytics, Salesforce and Tableau.

Director of Advanced Analytics

Led advanced analytics for Group Benefits and Group Retirement Solutions in Canada, managing a distributed team of data scientists and engineers across Canada and the Philippines. Secured business-unit funding and led resource planning.

• Revenue & pricing: Delivered $58M in revenue growth and cost savings through predictive modeling and advanced analytics, including disability-benefit pricing.
• Fraud & claims: Led fraud analytics delivering $9M in cost avoidance and recovery; optimized disability-claim intervention paths to release $500K in reserves annually.
• Underwriting & credit risk: Built mortality models to automate underwriting, saving $1.1M annually; reviewed and validated credit-risk scoring models for Manulife Bank.
• ML engineering & delivery: Led development of end-to-end ML pipelines for data ingestion into the enterprise data lake and model deployment in Hadoop; improved team efficiency through standardized code review, documentation and deployment practices.
• Customer growth & personalization: Led development of churn, customer lifetime value, propensity and Next Best Action models; secured CFO approval for an investment-product upsell pilot.
• Automation & vendor engagement: Managed robotic process automation (RPA) for quote generation based on likelihood of sale; led acquisition of Tableau Server and third-party datasets.

Tech Stack: Azure, Hadoop Hortonworks, Hive, Sqoop, NiFi, Oozie, Spark, Kafka, Jenkins, DB2, SQL Server, Salesforce and Tableau.

Data Science Manager

Led a data science team delivering advanced analytics to support operations, finance, planning and policy development.

• Optimized turf-maintenance resource allocation, reducing costs by 28% and saving $1.4M annually; redesigned needle-disposal analytics, reducing costs by 70% and saving $200K annually.
• Established the Open Data Science program with University of Alberta faculty and graduate students, expanding delivery capacity for projects involving structured and unstructured data.

Tech Stack: AWS, Oracle, SQL Server, PostgreSQL, Python, R, Ionic and Tableau.

Data Scientist

Conducted machine-learning and data-mining research for financial markets, focusing on time-series anomaly detection, securities fraud and predictive modeling.

• Developed a prediction-based contextual anomaly-detection method to identify potential stock-market manipulation, incorporating temporal patterns and relationships among comparable securities.
• Built and benchmarked supervised models for market-manipulation detection, including decision trees, random forests, neural networks and support vector machines.
• Designed evaluation experiments using historical market data and synthetic anomaly injection, assessing precision-recall trade-offs, class imbalance and computational scalability.
• Improved asset-return prediction using regularized regression for an institutional investment firm; introduced advanced data-mining methods to securities-fraud regulators, including IIROC.

Quant - Mitacs Accelerate Fellowship

Developed quantitative models and risk analysis for investment strategies supporting a $700M portfolio; researched equities and market anomalies. Fellowship undertaken concurrently with AMII research.

Earlier experience

• Senior Systems Analyst - University of Alberta, 2009 - 2010.
• Systems Analyst - IBM, 2008 - 2009.