Manager, Lending Strategy Reporting & Insights

BMO•Toronto, ON

1d•CA$69,000 - CA$129,000•Onsite

About The Position

Join a pioneering team shaping the future of Canadian Retail Credit Strategies. We’re building next-generation, end-to-end credit solutions that span the entire lifecycle—from acquisition and account management to collections—anchored in a holistic Lending Decision Strategy and aligned with Canadian Personal & Business Banking (P&BB) priorities. Our approach combines cutting-edge decisioning software, advanced decision trees, and innovative credit models to deliver smarter, faster, and more customer-centric outcomes. This is your opportunity to influence credit cycles using modern modeling techniques and best-in-class decisioning applications, all within a high-performance, customer-focused environment. If you’re passionate about leveraging data, technology, and strategy to transform lending decisions and drive meaningful impact across Canadian P&BB, this is the team for you. The Manager, Lending Strategy Reporting & Insights transforms data into decision‑ready insight across the customer credit lifecycle—from acquisition and underwriting through account management, fraud, and collections—across Consumer and Business Banking credit products as well as an accountability for Credit Risk Campaign Design & Measurement.

Requirements

3+ years of progressive experience in Credit Risk, Portfolio Management, Lending, or advanced analytics across consumer and/or business credit portfolios.
Hands-on experience with: Python (data transformation, automation)
Git/GitHub (version control, collaboration workflows)
SAS (ability to read, execute, and modify existing scripts)
Power BI (Power Query, DAX, data modeling, and dashboard development)
Demonstrated ability to translate complex data into strategic insights and influence stakeholders with concise, decision‑ready narratives.
Hands‑on expertise building monitoring frameworks, early‑warning indicators, behavioral segmentation, and performance measurement/back‑testing.
Strong stakeholder management skills, with the ability to communicate effectively, align expectations, and proactively identify and flag risks or gaps early rather than at delivery.
Strong business acumen connecting customer behavior, macro factors, operational signals, and portfolio outcomes; comfortable with trade‑off framing.
Excellent communication skills—able to brief senior audiences succinctly and coach junior analysts on storytelling with data.

Nice To Haves

Experience with credit decisioning strategies (e.g., limit management, pricing, treatment orchestration) and experimentation (A/B, champion/challenger).
Familiarity with model governance/model risk concepts and stress testing; experience integrating model outputs into monitoring and strategies.
Knowledge of collections operations, fraud operations, and hardship programs; exposure to macroeconomic scenario analysis.
Degree in a quantitative field (e.g., Statistics, Economics, Finance, Data Science); graduate degree an asset.

Responsibilities

Use Python and SQL to transform data into reusable, analytics-ready datasets.
Understand, execute, and maintain existing SAS scripts and reporting processes.
Analyze business logic embedded in SAS programs and ensure continuity and accuracy of outputs.
Translate SAS-based logic into Python-based workflows where appropriate.
Design and enhance monitoring frameworks including automated alerts, validation controls, and data-driven indicators to ensure accuracy and reliability.
Standardize and optimize data transformations for scalability, maintainability, and reuse.
Ensure datasets are structured and optimized for efficient consumption in Power BI reporting and dashboards.
Use Git/GitHub for version control, collaboration, and structured code management.
Ensure proper documentation, reproducibility, and auditability of analytics and reporting processes.
Apply best practices for code quality, modularity, and reusability across Python, SAS, and Power BI assets.
Ensure alignment with enterprise data governance, risk appetite, and regulatory expectations.
Develop a connected lifecycle view that links acquisition risk profiles, account‑management actions (e.g., limit management, pricing, treatments), fraud signals, payment performance, and collections outcomes.
Partner with Credit Risk, Product, Marketing, Fraud, and Collections to translate insights into strategy adjustments, treatment design, champion/challenger tests, or policy changes.
Stand up monitoring for new products, policy changes, and strategy deployments to enable early feedback loops and rapid course‑correction.
Design and develop Power BI dashboards and semantic models (Power Query, DAX) to deliver scalable, executive-ready reporting and insights.
Optimize Power BI data models and report performance for usability, scalability, and self-service analytics.
Distill complex analysis into concise narratives with a clear POV and next‑best actions; quantify expected impact and measurement plans.