Vice President – Credit Portfolio Analytics/ Vice-président, Analyses de Portefeuille de Crédit

BMO•Montreal, QC

About The Position

The Vice President, Credit Portfolio Risk Analytics strengthens Corporate Banking’s portfolio-level risk insight and monitoring capabilities through high-quality, forward-looking analytics. The role designs and executes standardized insight frameworks, produces executive-ready analysis of credit risk trends, and enhances the organization’s ability to identify shifts in credit quality, concentrations, and emerging vulnerabilities. The VP works closely with Corporate Bankers, Enterprise Risk Portfolio Management, and other key stakeholders across Corporate Banking to ensure consistent interpretation of portfolio performance and risk drivers.

Requirements

Undergraduate degree in Finance, Economics, Accounting or related field.
7+ years of experience in corporate lending, portfolio analytics, or credit risk management.
Deep understanding of Corporate Banking or Commercial Banking lending and portfolio management principles.
Demonstrated experience with data preparation, data analysis, and analytical toolsets such as SQL, SAS, Excel/VBA, and Power BI.
Exceptional analytical, quantitative, and conceptual thinking skills.
Ability to synthesize complex data into clear, concise deliverables for senior audiences.
Manage multiple projects simultaneously and deliver under tight timelines.
Strong written and verbal communication capabilities.
Highly developed interpersonal skills.
Strong attention to detail and ability to manage multiple priorities effectively.
Ability to work independently, adapt quickly, and bring creativity to complex analytical challenges.

Responsibilities

Deliver forward‑looking portfolio analysis, including migration patterns, concentrations, sector themes, problem‑account trends, and early‑warning indicators.
Conduct deep analysis across sectors, industries, regions, and key concentrations, including leveraged populations and other areas requiring focused monitoring.
Produce reporting that highlights emerging risks, drivers behind material changes, and other thematic insights.
Provide tailored analytics and data extracts to Business Sectors to help interpret changes in risk drivers, exposures, or segment‑specific profiles and trends.
Synthesize analytical outputs into clear options and implications to support decision‑making at senior forums.
Ensure analytical outputs are consistent with enterprise risk views and incorporate appropriate business, economic, and credit considerations.
Support data requirements for regulatory reviews, audit requests, data quality remediation, and portfolio‑level governance activities.
Strengthen data reliability by validating critical data elements, performing reconciliations, and maintaining robust controls around analytics.
Produce timely monthly and quarterly monitoring packages summarizing material portfolio trends, risk migration patterns, and emerging issues.
Serve as a subject matter expert for Corporate Banking and other stakeholders, supporting interpretation of portfolio behavior, trends, and risk insights.
Support analytics for specialized risk mandates, portfolio initiatives, evolving business needs, or new portfolio management techniques.
Support enhancements to credit‑risk measurement practices and early‑warning analytics.
Contribute to the modernization, automation, and usability of credit insights reporting and dashboards.
Demonstrate strong technical capability to extract, manipulate, and analyze large‑scale datasets using tools such as SQL, SAS, and Excel/VBA.
Apply advanced data‑wrangling techniques to convert raw or unstructured data into clean, structured inputs that support the portfolio analytics mandate.
Lead continuous improvements in data workflows, enabling efficient processing, enhanced data quality, and reduced reliance on manual intervention in recurring analytics.
Build and maintain repeatable, well‑documented data‑extraction pipelines that improve reliability, auditability, and traceability of analytics.
Champion the adoption of data governance best practices, ensuring clarity of lineage, transparent transformation logic, and strong quality assurance controls.