Owned enterprise analytics, data, application, and AI/model strategy as Fractional Chief Analytics Officer, providing executive oversight across Azure and Salesforce-enabled platforms, SaaS integrations, data products, predictive/model outputs, Power BI reporting, governance, and measurable business outcomes. • Developed and executed a 5-year analytics, data, and technology roadmap, aligning cloud platform investments, model development, data governance, security, compliance, and measurable business growth priorities. • Designed Azure-based architecture including Azure Data Lake/Fabric-ready data services, ETL/ELT pipelines, APIs, SQL data models, semantic reporting layers, and Power BI dashboards to support customer lifecycle analytics, payments, KYC, collections, and operational reporting. • Directed CRM/Salesforce and Azure integration patterns to support client data enrichment, automated KYC, payments processing, collections workflows, pricing handoffs, and enterprise reporting outside core SaaS platform limitations. • Built star/snowflake-style reporting schemas and model-ready datasets by joining relational entities, primary/foreign keys, customer attributes, transaction data, policy/contract data, payment events, and collections outcomes for downstream analytics and dashboards. • Created and evaluated AML models used for assessing customer, transaction, and entity-level financial crime risk; detecting suspicious activity patterns; improving alert prioritization; and supporting regulatory reporting, model governance, and executive risk oversight. • Created Visio data flow diagrams, data dictionaries, business glossary entries, lineage paths, API/interface documentation, control points, and Power BI semantic layer requirements to improve traceability and self-service analytics adoption. • Established data quality dashboards tracking completeness, accuracy, timeliness, duplicates, rejected records, reconciliation breaks, exception volumes, aging defects, failed controls, SLA adherence, and go-live readiness. • Provided end-to-end ownership of analytics and application delivery, overseeing strategy, architecture, implementation, data quality, model-ready datasets, dashboards, vendor platforms, UAT, adoption, production governance, executive reporting, and measurable business impact. • Directed SaaS/vendor platform lifecycle including requirements, API integration, SQL validation, test strategy, UAT execution, defect triage, deployment, post-production governance, and continuous improvement across Microsoft Azure, GoCardless, and Salesforce-enabled workflows. • Established data security, RBAC, data classification, approval workflows, privacy controls, AI ethics, and responsible AI standards across platform and analytics environments. • Built and mentored lean analytics and data teams of 5-10 resources, coaching analysts and engineers on SQL validation, data mapping, model interpretation, dashboard quality, stakeholder communication, and agile delivery practices. • Managed analytics and technology budgets, vendor relationships, contracts, delivery performance, RAID logs, sprint planning, backlog prioritization, release readiness, and executive reporting for cloud/data initiatives. • Acted as strategic analytics advisor to executives, translating model outputs, business metrics, data quality risks, and cloud implementation decisions into recommendations for platform investment, compliance, product scalability, and ROI.

Directed AML models, analytics and data governance across Global Risk and Financial Crime, overseeing the full model development life cycle from business requirements, data sourcing, feature/typology design, model build support, testing, validation evidence, deployment readiness, performance monitoring, tuning, governance, regulatory reporting, and audit remediation. • Led automation and integration of AML model alerts into Oracle-based and cloud reporting platforms, improving scalability, traceability, data quality, model monitoring, and regulatory reporting across risk systems. • Directed onboarding and enterprise integration of Quantifind and SymphonyAI SaaS solutions, enhancing AML/fraud monitoring through external data sources, entity risk signals, AI/ML tools, and real-time risk insights. • Owned the AML model development life cycle and analytics enablement roadmap, establishing OSFI E-23-aligned governance for model design, data sourcing, feature definition, validation support, model monitoring, data quality, reporting, and executive oversight. • Supported Oracle-to-Google Cloud migration activities for FCRM and AML datasets, defining ingestion, transformation, reconciliation, metadata, access-control, retention, and lineage requirements for migrated risk data. • Created source-to-target mapping, data flow, metadata, business glossary, and lineage documentation connecting Oracle ERP structures to cloud repositories, reporting layers, and downstream risk analytics use cases. • Built SQL and Power BI data quality reports to monitor completeness, accuracy, exception trends, control breaches, unresolved defects, aging issues, and executive readiness indicators. • Led a cross-functional team of approximately 15 senior managers, analysts, engineers, and governance specialists delivering enterprise analytics, model monitoring, vendor integration, and data governance initiatives. • Directed portfolio planning, budget management, RFP/SOW approvals, vendor onboarding, POC activities, privacy assessments, resource forecasting, and production readiness for analytics and AI/ML platforms. • Designed and implemented enterprise data governance operating model including ownership, stewardship, controls, data quality rules, critical data elements, lineage, metadata, cataloging, and model input accountability. • Led AML model development, evaluation and usability reviews, assessing model performance, alert quality, false positive trends, threshold behavior, typology coverage, explainability, investigator usability, and business relevance to improve detection effectiveness and executive risk oversight. • Partnered with AML operations, investigators, model developers, data scientists, technology, and governance teams to ensure model outputs were practical, interpretable, actionable, regulator-ready, and aligned with case investigation and executive risk objectives. • Established model governance documentation covering model purpose, input variables, assumptions, limitations, data lineage, control points, testing results, performance metrics, change logs, validation evidence, and audit-ready remediation plans. • Advised executives, business leaders, and regulators on analytics governance, model risk, AI ethics, data quality, responsible AI, FINTRAC remediation, and evidence preparation for audit and regulatory review.

Led retail risk analytics, risk forums, executive reporting, and data platform modernization across enterprise risk, overseeing governed SQL Server data warehouses, KPI dashboards, PowerPoint risk packs, model-ready datasets, and OSFI/BCBS 239-aligned data controls to support senior leadership decisions and regulatory reporting. • Directed migration from mainframe JEWEL, SAS datasets, CRM feeds, and legacy risk databases to SQL Server, documenting source-to-target mappings, transformation logic, aggregation rules, reconciliation controls, and data quality thresholds. • Repositioned legacy mainframe-style operational data concepts into SQL server and AWS cloud, documenting source-to-target mappings, transformation logic, cleansing rules, reconciliation controls, and migration acceptance criteria for analytics-ready data products. • Built and maintained enterprise risk dashboards, KPI repositories, business logic, data dictionaries, lineage documentation, and Power BI/Tableau reports to monitor delinquency, provisions, recoveries, write-offs, risk appetite, and portfolio performance. • Led monthly retail risk forums with senior risk, collections, analytics, technology, audit, and business stakeholders to review portfolio performance, delinquency trends, model/reporting outputs, data quality issues, risk thresholds, and remediation actions. • Created executive PowerPoint decks and risk committee materials summarizing portfolio trends, KPI movements, credit risk exposure, collections performance, emerging risks, data quality issues, and recommended management actions. • Presented SQL/SAS/Python-driven risk analytics findings to senior leadership, translating complex data outputs into clear business narratives, decision points, and action plans for risk mitigation, portfolio management, and regulatory readiness. • Enabled acquisition, cohort, delinquency, write-off, recovery, insolvency, and collections lifecycle analytics by improving data availability, accuracy, timeliness, and adaptability across critical risk data elements. • Investigated fraud patterns and enhanced fraud detection models using retail transaction data, improving identification of high-risk behaviors and supporting risk mitigation strategies. • Partnered with business sponsors, technology, OCDO, audit, OSFI stakeholders, data stewards, and architecture teams to define requirements, controls, ownership, governance, and remediation priorities. • Led testing activities including test strategy, SQL validation, data reconciliation, regression testing, defect management, and UAT sign-off to ensure migrated data supported models, forecasts, and regulatory reporting. • Maintained Jira backlogs, project plans, budgets, resource plans, issue logs, change requests, and steering committee reporting, escalating risks and dependencies through PMO channels. • Led a team of 25+ internal and external resources across data, analytics, reporting, governance, and technology delivery. • Partnered with the Office of the Chief Data Officer to define critical data elements, stewards, data ownership, dictionaries, lineage, quality thresholds, issue remediation processes, and governance procedures across retail risk business lines.

PMP

PMI

2011 - 2029