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Added 5 mins ago
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BMO

Manager - Lending Strategy

Toronto, ON, Canada
BMOPLACE, Canada

Pay: $70,000.00 - $150,000.00
Job type: Full-time
Apply by: Oct 14, 2026
Experience: 3+ years of experience in Data Science, Machine Learning, Advanced Analytics, Credit Risk Analytics, or Unsecured Lending Strategy.
Posting language: English
Posted: Sep 23, 2026

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Original job description

Application Deadline:

10/13/2026

Address:

33 Dundas Street West

Job Family Group:

Audit, Risk & Compliance

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, 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

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.

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More about this job

Responsibilities

The manager will develop advanced machine learning and AI-enabled capabilities to optimize lending strategies across credit card and personal lending portfolios. They will translate data science innovations into practical applications for acquisition, account management, and risk mitigation while collaborating with cross-functional partners.

Requirements

Candidates must have at least 3 years of experience in data science, credit risk analytics, or lending strategy. A graduate degree in a quantitative field such as statistics, mathematics, or computer science is preferred.

Skills

Data science
Machine learning
Predictive modeling
Credit risk analytics
Python
SQL
SAS
Strategy optimization
Decision science
Feature engineering
Statistical modeling
Generative AI
Data visualization
Stakeholder management
Portfolio management
Unsecured lending

Benefits

Health insurance
Tuition reimbursement
Accident insurance
Life insurance
Retirement savings plans
Performance-based incentives
Discretionary bonuses

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Machine Learning
Artificial Intelligence
Predictive Modeling
Credit Risk
Unsecured Lending
Python
SQL
SAS
Strategy Optimization
Decision Trees
Feature Engineering
Generative AI
Big Data
Portfolio Management
Champion-Challenger Testing
Risk Appetite
Data Mining
Natural Language Processing
Cloud Analytics
Model Governance
Regulatory Compliance
Financial Services
Customer Segmentation
Forecasting
Performance Measurement
Stakeholder Management
Operations Research
Quantitative Analysis
Credit Lifecycle

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