Date limite pour présenter sa candidature :

11/15/2026

Adresse :

33 Dundas Street West

Groupe de famille d'emploi :

Audit, gestion des risques et conformité

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 contemporary 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, Unsecured Lending & Strategy Optimization is responsible for developing advanced analytical, machine learning, and AI-enabled capabilities to improve strategy effectiveness across Credit Cards and Personal Lending portfolios.

The role applies large-scale structured and unstructured data to forecast strategy outcomes, uncover emerging risk and growth signals, identify customer and portfolio trends, and build decision frameworks that balance profitable growth, enhanced customer experience, and the bank’s risk appetite.

As part of Unsecured Lending Strategy team, the Manager translates data science innovation into practical strategy applications across acquisition, account management, exposure optimization, customer engagement, early warning, and portfolio performance management, while collaborating with Product, Risk, Technology, Analytics and Operations partners to support execution.

Key AccountabilitiesData Science & Predictive Modeling

- Support to develop and apply machine learning, statistical, and AI-enabled models to improve strategy decision-making across the customer lifecycle.
- Mine large-scale structured and unstructured data to identify new predictive variables, customer attributes, behavioural signals, and early warning indicators.
- Design feature engineering approaches that improve decision segmentation, customer targeting, pricing, limit management, and treatment optimization.
- Apply Build predictive frameworks to estimate customer response, credit performance, profitability, attrition, engagement, and other strategy outcomes.
- Evaluate the incremental value, stability, explain ability, and business usability of new variables before recommending use in strategy decisioning.
- Create simulation and forecasting capabilities to assess the expected financial and risk impact of strategy changes before implementation.
- Support the exploration and testing of AI and other analytical tools to improve strategy decision-making and portfolio management, with a focus on practical business applications and appropriate governance.
- Act as a key model user, applying models and scores within strategy logic, ensuring proper interpretation and segmentation to differentiate risk across the portfolio.

Strategy Optimization & Decision Science

- Design and implement customer-level lending strategies using decision tree and optimization platforms, aligning treatments to growth, profitability, customer experience, and risk objectives.
- Contribute to strategy design across acquisition, credit limit management, exposure optimization, early warning treatments, and performance-based interventions, ensuring alignment across the customer lifecycle.
- Execute structured Champion-Challenger testing frameworks to evaluate and refine treatments, segmentation schemes, decision pathways, and business rules.
- Collaborate with Technology and Analytics teams to translate strategy logic into deployable decision rules, ensuring technical feasibility, operational integrity, and governed execution.
- Support the evaluation and testing of AI and emerging analytical tools to improve strategy decision-making and portfolio management, with a focus on practical use cases, business value, and appropriate governance.

Governance, Risk Management & Stakeholder Influence

- Knowledge of strategy, analytics, and AI-enabled capabilities and their application within credit policy, model governance, responsible AI, privacy, regulatory, and operational risk frameworks is considered an asset.
- Monitor portfolio performance, emerging trends, and risk-return dynamics; recommend actions that keep outcomes align

📌 Manager - Lending Strategy (Toronto)
🏢 BMO Financial Group
📍 Toronto