100 King Street West Toronto Ontario,M5X 1A1

###Role Summary

###BMO’s Enterprise Risk group is seeking a hands-on Credit Risk Analyst to develop, calibrate, deploy, and monitor regulatory Probability of Default (PD) models for wholesale portfolios. With a primary emphasis on data science and model development ( 60%) and complementary ownership of production engineering and MLOps ( 40%), you will translate complex business requirements and regulatory expectations into robust, explainable, and auditable solutions. The ideal candidate blends quantitative rigor with strong comprehension, communication, critical thinking, and collaboration skills.

###Key Responsibilities

###A) Data Science & Modelling – ~60%

• ###Own the end-to-end lifecycle of wholesale PD models (scoping, data understanding, feature engineering, model development, calibration, performance measurement, documentation, implementation support, and ongoing monitoring).
• ###Design PD modelling strategies across wholesale segments (e.g., corporate, commercial, financial institutions, specialized lending), selecting appropriate techniques (e.g., logistic/GLM, survival/PD term structures, regularization) with clear rationale and interpretability.
• ###Engineer features from internal/external data (financial statements, rating histories, obligor/industry attributes, collateral/covenants, macroeconomic drivers) ensuring lineage, quality, and reproducibility.
• ###Establish and track discrimination, calibration, and stability metrics; perform back-testing and benchmarking; develop champion–challenger frameworks and sensitivity/stress analyses.

###Produce high-quality model documentation tailored to model validation, internal audit, and regulators; communicate complex concepts clearly to technical and non-technical stakeholders.

###B) Machine Learning Engineering & Delivery – ~40%

• ###Develop models on AWS (e.g., SageMaker, S3, Glue, EMR/Athena, Lambda/Step Functions) with CI/CD, version control, and automated testing (unit, integration, UAT).
• ###Package and expose scoring services and monitoring jobs (e.g., Python APIs with FastAPI/Flask; batch pipelines) following secure-by-design patterns and enterprise standards.
• ###Implement data pipelines and model monitoring (data drift, performance drift, stability, alerts) with robust logging, lineage, and access controls.
• ###Partner with Data Engineering and Technology to align on architectures (containerization with Docker; orchestration; model registry) and ensure reliability and scalability.

###Domain Focus: Regulatory Wholesale Credit Risk

• ###Develop PD models aligned to wholesale credit risk regulations (stress testing, allowance).
• ###Support regulatory and accounting use cases (e.g., CCAR – DFAST stress testing, IFRS 9 provisioning, CECL provisioning) and model risk management expectations across the model lifecycle.
• ###Collaborate closely with stakeholders across Risk, Lines of Business, Model Validation, Internal Audit, and Compliance during reviews, findings remediation, and regulatory exams.

###Required Qualifications

###3+ years of hands-on experience building and implementing credit risk models—ideally wholesale PD—with measurable business impact.

###Advanced proficiency inPython(e.g., pandas, NumPy, scikit-learn, statsmodels, XGBoost/lightGBM as appropriate).

###Working proficiency inSASfor data preparation, analytics, and/or model implementation within enterprise environments.

###Practical experience onAWS(e.g., S3, Glue, EMR/Athena, SageMaker, Lambda/Step Functions) for data processing and model deployment.

###Strong understanding of statistical learning, feature engineering, validation techniques, and performance monitoring for PD models.

###Proven ability to author clear, regulator-ready documentation and to present complex analyses to senior stakeholders.

###Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Economics/Finance); graduate degree preferred.

###Preferred / Nice to Have

• ###Experience with wholesale data domains (financial spreading, internal ratings, default events, collateral/covenants, industry taxonomy) and linking to macroeconomic variables.
• ###Exposure to MLOps tooling (e.g., MLflow/Feature Store, Docker/Kubernetes, Git, CI/CD) and workflow orchestration (e.g., Airflow/Step Functions).
• ###Working knowledge of SQL and distributed data processing (e.g., Spark); experience with SAS macros is an asset.
• ###Familiarity with model risk management practices and regulatory expectations across the lifecycle; experience engaging model validation, audit, and regulators.

###Meta Skills & Ways of Working (Core to Success)

• ###Comprehension: rapidly absorb complex business and regulatory context; ask incisive questions to frame the real problem.
• ###Communication: tailor messages for technical peers, senior executives, validators, and regulators; write clear, concise documentation.
• ###Critical Thinking: evaluate trade-offs between interpretability and performance; design tests to falsify assumptions; identify and mitigate model risk.
• ###Collaboration: co-create with partners across Risk, Business, Data Engineering, and Technology; invite diverse perspectives and build consensus.

Salary:

$67,200.00 - $124,200.00

Pay Type:

Salaried

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, 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.

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

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.