Lead Data Scientist, Canadian Commercial Banking

Title:

Lead Data Scientist, Canadian Commercial Banking

Requisition

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

As a Lead Data Scientist within Commercial Banking Data and Analytics at Scotiabank, you will play a critical role in the stewardship, stability, enhancement, and ongoing operation of a business-critical analytics and predictive modeling platform supporting Commercial Banking and Risk stakeholders.

In this role, you will provide technical leadership across complex data assets, predictive models, business rules, and production processes. You will work through ambiguity, reverse engineer existing methodologies and code, assess data and model risks, and partner across Analytics, Credit, Risk, Data, and Technology teams to strengthen analytical capabilities that help enable sound business decisions.

• Assume technical ownership of a critical enterprise analytics platform, including its predictive models, methodologies, data assets, business rules, code base, and production processes
• Rapidly acquire platform knowledge and independently determine how complex, interconnected components operate, including areas with limited documentation or institutional knowledge
• Lead platform stabilization by identifying key data, model, process, and operational risks; prioritizing remediation; and establishing a sustainable support model
• Own model stewardship, including performance monitoring, methodology assessment, recalibration, enhancements, controls, and support for governance and validation activities
• Investigate and resolve complex data quality, model output, pipeline, and production issues through structured root cause analysis
• Develop a detailed understanding of data lineage, transformations, feature logic, source-system dependencies, and downstream uses; ensure that material changes are assessed and documented
• Maintain clear documentation of data sources, model methodologies, code, controls, operating procedures, issue resolution, and key business rules
• Partner with Commercial Banking, Credit, Risk, Data, and Technology stakeholders to ensure the platform remains reliable and fit for purpose
• Provide technical direction across the cross-functional resources supporting the platform, with clear accountability across data, modeling, credit, and operational activities
• Establish repeatable development, testing, release, monitoring, and incident-management practices that strengthen resiliency and reduce key-person dependency
• Identify opportunities to simplify and modernize legacy analytical processes and, as capacity allows, contribute to broader advanced analytics and AI initiatives
• Mentor data scientists and analysts and promote high standards for analytical rigor, documentation, reproducibility, and responsible model use

• Extensive experience in data science, predictive modeling, advanced analytics, or a related quantitative discipline, including accountability for production analytical assets
• Proven experience assuming ownership of a complex analytical platform, predictive model, or enterprise data asset and successfully stabilizing and operationalizing it
• Demonstrated ability to rapidly understand complex or partially documented business processes, data structures, model methodologies, code bases, and system dependencies
• Deep expertise in statistical modeling and machine learning, including model performance assessment, monitoring, recalibration, feature evaluation, validation support, and interpretation
• Strong experience with large-scale, multi-source enterprise datasets, including data lineage, quality investigation, transformation logic, and reproducible analysis
• Advanced Python and SQL skills for data investigation, feature engineering, model development, testing, and production support; GCP and Big Query experience is preferred
• Strong understanding of production data and ML pipelines, orchestration, version control, testing, deployment practices, and collaboration with data engineering and technology teams
• Experience with model governance, model risk management, documentation, controls, and audit or validation support in a regulated environment
• Strong structured problem-solving skills and sound judgment when working through ambiguous, high-impact technical or business issues
• Excellent communication skills, including the ability to explain complex data and model concepts to senior business, Risk, Credit, and technical stakeholders
• Demonstrated ability to provide technical leadership, influence cross-functional teams, and mentor data professionals without formal reporting authority
• Experience in financial services, commercial banking, credit risk, portfolio management, or early warning analytics is strongly preferred
• Experience applying GenAI, large language models, agentic workflows, or AI-assisted development is an asset, but secondary to deep data and modeling experience

• Experience in financial…