Senior Manager, Provisioning Methodology & Data Science

BMO Financial Group; Toronto; On-site; CAD 103,000 - 192,000; Full time

Job summary

A leading financial institution in Toronto is seeking a Sr. Manager, Provisioning Methodology & Data Science to lead advanced modeling initiatives. This role involves developing machine learning models for credit risk, supporting business processes, and collaborating across teams. The ideal candidate has over 5 years of experience in analytics and risk modeling. Competitive salary and comprehensive benefits are offered.

Benefits

Health insurance

Tuition reimbursement

Retirement savings plans

Accident and life insurance

Qualifications

• 5+ years of experience in credit risk modeling and analytics.
• Strong foundation in traditional statistics and advanced machine learning techniques.
• Experience building and validating credit risk models (PD, LGD, EAD).

Responsibilities

• Design, develop and implement advanced statistical and machine learning models.
• Create and maintain performance metrics.
• Support data team in new data acquisition efforts.

Skills

Proficiency in Python

Proficiency in Spark

Proficiency in SAS

Strong analytical thinking

Excellent verbal communication

Excellent written communication

Tools

Big data processing tools

Job description

Sr. Manager, Provisioning Methodology & Data Science

Application Deadline: 11/28/2025

Address: 33 Dundas Street West

Job Family Group: Data Analytics & Reporting

We are transforming the way BMO North American Retail Risk approaches loss forecasting and provisioning. As Sr. Manager, Provisioning Methodology & Data Science, you will leverage 5+ years of experience in credit risk modeling and analytics to lead the development, testing, implementation, maintenance and monitoring of advanced statistical and Machine Learning models focused on forecasting aged and insolvency loss components (PD, LGD and EAD), which support the Bank’s PCL (Provision for Credit Losses) calculations and enable early signing insights for the Credit Strategy Team.

The incumbent will have an excellent grasp of the modern techniques of data science in a big data environment, including but not limited to the mathematics and statistics of quantifying portfolio loss distributions through short- and long-term timeframes. He/she will have a thorough understanding of how models and advanced analytic solutions can be leveraged to support model owners (MO) and model users (MU) in measuring the performance of retail portfolios, and how these solutions can be used in conjunction with pricing, adjudication, account acquisition, marketing, product and strategy development, and customer management to optimize risk-return. He/she plays a key role interacting and building relationships with internal and external stakeholders and regulators in terms of model use, risk control and model risk management.

Key Responsibilities

• Model Development
  • Design, develop and implement advanced statistical and machine learning models to forecast aged defaults and insolvency losses across retail portfolios (e.g., cards, mortgages, small business banking).
  • Develop macro-senstive insolvency/aged components to support readiness for macroeconomic scenario analysis and financial disaster recovery planning.
• Model Implementation
  • Provide hand‑over document to model implementation team to implement models and conduct UAT.
  • Support data team in new data acquisition efforts during model implementation.
• Loss Forecasting and Business Use of Models
  • Refine stitching and provisioning methodologies to combine various model results together to generate loss forecast and ensure alignment with evolving regulatory and strategic requirements.
  • Provide support to model users when models are used in downstream business processes.
• Ongoing Model Performance Monitoring
  • Create and maintain performance metrics and assessment guidelines to monitor model accuracy, stability, and business impact.
  • Conduct ongoing model performance monitoring to ensure that models are performing as expected.
  • Perform trigger reviews in response to model performance issues.
  • Remediate model issues such as required.
  • Conduct recalibration or redevelopment as required.
• Documentation & Governance
  • Document model development processes, assumptions, and testing results to ensure transparency, validation and audit readiness.
  • Submit models to Model Risk (MR) team and answer MR’s model validation questions and secure MR’s approval.
  • Obtain exception approval from MR for model use on a time‑bounded exceptional basis in cases when the model results are needed urgently.
  • Partner with model owner, LOB and finance teams to ensure alignment with enterprise‑wide risk and finance strategies.
  • Stay abreast of the Bank’s new Model Risk management requirements and ensure compliance.
• Risk Insights Support
  • Support feature importance analysis to identify early indicators of default and insolvency, enabling proactive risk mitigation to minimize future loss.
  • Support the Credit strategy team to translate analytical insights into actionable strategies.
  • Support monitoring risk transition across segments to detect early warning signals for aged defaults and insolvency.
  • Support the development of tools and dashboards to visualize risk trends and support decision‑making.
  • Support risk assessment and controls to design metrics that minimize PCL exposure.
• Leadership & Collaboration
  • Champion a collaborative, agile working culture across cross‑functional teams.
  • Mentor junior analysts and contribute to a high‑performance modeling environment.

Qualifications

• Technical Skills
  • Proficiency in Python, Spark and SAS for big data processing.
  • Strong foundation in traditional statistics and advanced machine learning techniques.
  • Experience building and validating credit risk models (PD, LGD, EAD) and loss forecasting tools.
• Analytical & Communication Skills
  • Strong analytical thinking with the ability to translate complex data into actionable insights.
  • Excellent verbal and written communication skills, with a focus on cross‑group collaboration.
• Strategic Mindset
  • Proven ability to drive innovation in risk modeling and provisioning methodologies.
  • Experience working in fast‑paced environments with evolving priorities.
• Domain Knowledge
  • Familiarity with Retail Banking Products and the customer behaviours that surround them.
  • Familiarity with the credit management landscape and how strategies get formulated.

Salary

$103,200.00 - $192,000.00

Pay Type

Salaried

Benefits

BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details, please visit https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in‑depth training and coaching, to manager support and network‑building opportunities, we’ll help you gain valuable experience, and broaden your skillset.

BMO is committed to an inclusive, equitable and accessible workplace. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

To find out more visit us at https://jobs.bmo.com/ca/en