Title: Lead Data Scientist, Canadian Commercial Banking
Requisition ID: 271520

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

About the Opportunity:
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.

How You Will Make an Impact:

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

What You Will Bring:

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 BigQuery 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

Nice-to-have:





Experience in financial services, commercial banking, credit risk, portfolio management, or early warning analytics

Experience with GCP, BigQuery, or similar cloud-based data platforms

Experience applying GenAI, large language models, agentic workflows, or AI-assisted development

Familiarity with credit, risk, or commercial banking data ecosystems and related business processes

Education:

University degree in a relevant quantitative discipline such as Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field. A Master’s degree or PhD is preferred.

What’s in it for You?
When you join the bank, you are not just taking a job, you are becoming part of a culture that values inclusion, accountability, integrity, and client-centricity — that is our ScotiaBond, the foundation for how we support our people and deliver for our clients.

A competitive Total Rewards package including performance-based bonus and versatile benefits

Ongoing career development and coaching from experienced leaders, with opportunities to grow within Commercial Banking, analytics, technology, and across Scotiabank

The opportunity to lead meaningful work on business-critical analytics and predictive modeling platforms that support Commercial Banking and Risk decision-making

A dynamic, collaborative, and high-performing team that values partnership, inclusion, analytical rigor, and shared success

Exposure to complex data ecosystems, advanced analytics, model governance, and cross-functional business challenges that will accelerate your learning and impact

Working Conditions:

Work in a standard office-based environment; Additional hours may occasionally be required to support critical production issues, releases, or business priorities.

Location(s): Canada : Ontario : Toronto

Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.

At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know.

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📌 Lead Data Scientist, Canadian Commercial Banking (Ontario)
🏢 Scotiabank
📍 Ontario