Application Deadline:

10/13/2026

Address:
33 Dundas Street West

Job Family Group:

Audit, Risk & Compliance

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 Senior 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 Accountabilities
Data Science & Predictive Modeling

• 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.
• Build predictive frameworks to estimate customer response, credit performance, profitability, attrition, engagement, and other strategy outcomes.
• Evaluate the incremental value, stability, explainability, 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.
• Apply AI innovation and emerging capabilities by evaluating and piloting machine learning, Generative AI, big data, and optimization techniques that enhance strategy decisioning and portfolio management, with a focus on scalable use cases, measurable business value, implementation readiness, 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.
• Evaluate and pilot AI, Generative AI, machine learning, big data, and optimization capabilities that enhance strategy decisioning and portfolio management, with focus on scalable use cases, measurable business value, implementation readiness, and appropriate governance.

Governance, Risk Management & Stakeholder Influence

• Ensure strategy, analytics, and AI-enabled capabilities align with credit policy, model governance, responsible AI, privacy, regulatory, and operational risk requirements.
• Monitor portfolio performance, emerging trends, and risk-return dynamics; recommend actions that keep outcomes aligned to risk appetite and business objectives.
• Translate complex analytical findings into decision-ready recommendations for senior stakeholders, clearly outlining trade-offs, risks, and expected business impact.
• Influence Product and Risk partners through evidence-based recommendations, clear execution implications, and strong understanding of portfolio constraints.
• Serve as a subject matter expert on data science, decision science, and strategy optimization within Unsecured Lending Strategy.

Qualifications & Experience

• 3+ years of experience in Data Science, Machine Learning, Advanced Analytics, Credit Risk Analytics, or Unsecured Lending Strategy.
• Graduate degree preferred in Statistics, Data Science, Mathematics, Computer Science, Operations Research, Engineering, Finance, or a related quantitative field.
• Proven experience developing, applying, or operationalizing machine learning, statistical, AI, or optimization techniques to solve complex business problems.
• Strong knowledge of predictive modeling, feature engineering, experimentation design, model monitoring, segmentation, forecasting, and performance measurement.
• Experience with large-scale structured and unstructured datasets is an asset, including data mining, text mining, natural language processing, or Generative AI applications.
• Hands-on proficiency with Python, SQL, SAS, machine learning libraries, cloud-based analytics tools, and data visualization platforms.
• Strong understanding of unsecured lending products, credit lifecycle economics, risk-return trade-offs, portfolio management, and customer treatment strategies.
• Familiarity with model governance, responsible AI, privacy, credit policy, and regulatory expectations within a financial institution.
• Demonstrated ability to translate analytical innovation into practical strategy recommendations and measurable business outcomes.
• Strong communication, influencing, and stakeholder management skills, including the ability to explain technical concepts to senior business audiences.

Salary:

$70,000.00 - $150,000.00

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

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