Director, Decision Science - Canada

BMO•Toronto, ON

9d•Hybrid

About The Position

The Director, Decision Science – Canada will lead a team of decision scientists responsible for developing, deploying, and optimizing credit risk models across the customer lifecycle. This role owns adjudication, account management, and collections analytics, ensuring models are production‑ready, compliant, and deliver strong business outcomes. The ideal candidate brings deep credit risk expertise, strong people leadership, and the ability to apply advanced analytics, ML, and AI where appropriate. Applies mathematical and statistical methods to financial and risk management problems (e.g. internal controls; enterprise-wide stress testing and scenario analysis; capital modelling; valuations). Through quantitative analytical modelling, identifies important factors to consider for financial disaster and recovery plans. Conducts research and creates tools that use data to develop scenario-based planning and implements complex mathematical models to help the business make better financial and financial decisions (e.g. investments, pricing, etc.), drive innovation and minimize the impact of uncertainty. Develops pricing and quantitative risk models for an assigned portfolio e.g. fixed income, corporate credit and loans. Monitors risk in strategies and portfolios alongside project managers or functional leads. Conducts research and develops tools that use data to make better financial decisions; such as: investments, pricing, etc. Applies knowledge of risk assessment and controls along with extensive understanding of industry compliance standards and regulations. Identifies ways of mitigating potential risks; recommends and implements solutions based on analysis of issues and implications for the business. Documents data flow, systems and processes to improve the design, implementation and management of business/group processes. Conducts quantitative research in risks across strategies and portfolios. Operates at a group/enterprise-wide level and serves as a senior specialist resource across BMO. Influences how teams/groups work together. Applies expertise and thinks creatively to address unique or ambiguous situations and to find solutions to multiple, interdependent, complex problems. Communicates abstract concepts in simple terms. Fosters strong internal and external networks and works with and across multiple teams to achieve business objectives. Anticipates trends and responds by implementing appropriate changes. Broader work or accountabilities may be assigned as needed. Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.

Requirements

MS or PhD in Statistics, Mathematics, Economics, Data Science, Computer Science, or related field.
7+ years of experience in credit risk modeling within financial services or fintech.
Proven experience building and deploying models for adjudication, account management, and collections.
Strong expertise in data cleaning, reject inference, WoE binning, and credit bureau data usage.
Hands‑on experience taking models from design through production implementation.
Proficiency with analytics tools such as Python, R, SQL, or SAS.
Demonstrated people leadership and ability to influence cross‑functional partners.
Advanced level of proficiency: Regulatory capital and stress testing.
Advanced level of proficiency: Compliance and regulation.
Advanced level of proficiency: Machine learning.
Advanced level of proficiency: Learning Agility.
Expert level of proficiency: Model risk management.
Expert level of proficiency: Data visualization.
Expert level of proficiency: Data wrangling.
Expert level of proficiency: Data preprocessing.
Expert level of proficiency: Critical thinking.
Expert level of proficiency: Driving Results.
Expert level of proficiency: Quantitative financial modeling.
Expert level of proficiency: Computational thinking and programming.
Expert level of proficiency: Verbal & written communication skills.
Expert level of proficiency: Analytical and problem solving skills.
Expert level of proficiency: Collaboration & team skills; with a focus on cross-group collaboration.
Expert level of proficiency: Able to manage ambiguity.
Expert level of proficiency: Data driven decision making.
Typically 9+ years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.

Nice To Haves

Experience with ML/AI models in regulated credit environments.
Familiarity with Canadian credit market and regulatory expectations.
Exposure to modern MLOps and cloud‑based analytics platforms.

Responsibilities

Lead, mentor, and develop a high‑performing team of 10+ decision science professionals.
Own end‑to‑end development of underwriting, account management, and collections models.
Drive best‑in‑class modeling practices including reject inference, WoE binning, IV analysis, and scorecard development.
Partner with Technology and Product to ensure seamless model‑to‑production deployment, monitoring, and governance.
Oversee data preparation, feature engineering, and rigorous data cleaning using internal and credit bureau data.
Monitor model performance, stability, and drift; lead recalibration and enhancement efforts.
Apply machine learning and AI techniques selectively to improve predictive power while maintaining explainability and regulatory alignment.
Communicate insights and recommendations to senior leadership and key stakeholders.
Fosters a culture aligned to BMO purpose, values and strategy and role models BMO values and behaviours in all that they do.
Ensures alignment between values and behaviour that fosters diversity and inclusion.
Regularly connects work to BMO’s purpose, sets inspirational goals, defines clear expected outcomes, and ensures clear accountability for follow through.
Builds interdependent teams that collaborate across functional and operating groups to create the highest value for all stakeholders.
Attracts, retains, and enables the career development of top talent.
Improves team performance, recognizes and rewards performance, coaches employees, supports their development, and manages poor performance.