Manager, Lending Strategy, Reporting Insights

BMO•Toronto, ON

1d•CA$69,000 - CA$129,000•Onsite

About The Position

The Manager, Credit Risk 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 Credit Lifecycle Analytics & Decision Support. 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. Build executive‑ready stories and dashboards that surface risks, opportunities, and trade‑offs; tailor messages for forums such as Product/Credit Councils, Finance reviews, and Risk updates. Distill complex analysis into concise narratives with a clear POV and next‑best actions; quantify expected impact and measurement plans. Apply advanced analytics (behavioral scoring, predictive indicators, segmentation, survival/roll‑rate modeling, stress indicators) to improve early‑warning and decision quality. Enhance monitoring tools (dashboards, automated alerts, predictive flags) and ensure robust measurement/back‑testing so recommendations are evidence‑based. Partner with model developers and users to operationalize model insights within monitoring and treatment logic; ensure fit‑for‑use and champion/challenger evaluation. Ensure monitoring and insights align to enterprise risk appetite and comply with internal policies and regulatory expectations.

Requirements

3+ years of progressive experience in Credit Risk, Portfolio Management, Lending, or advanced analytics across consumer and/or business credit portfolios.
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 business acumen connecting customer behavior, macro factors, operational signals, and portfolio outcomes; comfortable with trade‑off framing.
Proficiency with Advanced SAS/SQL, Power BI (Power Apps, Power Query, DAX Coding) for dashboarding and automated reporting.
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

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.
Build executive‑ready stories and dashboards that surface risks, opportunities, and trade‑offs; tailor messages for forums such as Product/Credit Councils, Finance reviews, and Risk updates.
Distill complex analysis into concise narratives with a clear POV and next‑best actions; quantify expected impact and measurement plans.
Apply advanced analytics (behavioral scoring, predictive indicators, segmentation, survival/roll‑rate modeling, stress indicators) to improve early‑warning and decision quality.
Enhance monitoring tools (dashboards, automated alerts, predictive flags) and ensure robust measurement/back‑testing so recommendations are evidence‑based.
Partner with model developers and users to operationalize model insights within monitoring and treatment logic; ensure fit‑for‑use and champion/challenger evaluation.
Ensure monitoring and insights align to enterprise risk appetite and comply with internal policies and regulatory expectations.