AI Engineer - Decision Science

BMO; Toronto; On-site; CAD 80,000 - 175,000; Full time

Benefits offered by this job

Health insurance

Tuition reimbursement

Accident and life insurance

Retirement savings plan

Job summary

BMO seeks an AI Engineer – Decision Science to design and deploy AI-driven risk decisioning across credit, fraud, and enterprise risk. You will implement advanced analytics, sentiment analysis, and anomaly detection, leveraging LLMs, NLP, and automated workflows.

Strong data handling and governance experience is essential to ensure compliant, explainable models. The role requires collaboration with Risk, Credit, Fraud and Finance teams to translate business needs into scalable AI solutions and

Qualifications

• Master's degree or higher in statistics, mathematics, CS, engineering, data science or a related field.
• 3+ years of ML/AI or advanced analytics experience in financial services or risk domains.
• Hands-on experience building and deploying ML/AI models for credit risk, fraud, or enterprise risk.
• Strong programming in Python, SQL, and SAS.
• Experience with decision science frameworks or automated decision systems.
• Familiarity with LLMs and AI-based automation (NLP, agent workflows).
• Solid model governance, validation and regulatory understanding.
• Experience handling large structured and unstructured datasets.

Responsibilities

• Design and deploy AI/ML-driven decision science solutions for enterprise risk (credit adjudication, collections, loan review, monitoring).
• Build automated decisioning to improve speed, consistency and accuracy of risk decisions.
• Develop analytics: sentiment analysis, behavioral modeling, fraud/ anomaly detection, risk scoring.
• Design agentic AI workflows using external LLMs and build AI tools for documentation, knowledge retrieval and workflow automation.
• Ensure governance, security, explainability and responsible AI standards.

Skills

Machine Learning

AI/Decision Science

Python

SQL

SAS

LLMs

Governance & Compliance

Data Analysis

Education

Master's degree in a quantitative field

Tools

Python

SQL

SAS

NLP

Spark/Databricks

Job description

Application Deadline:

09/18/2026

Address:

33 Dundas Street West

Job Family Group:

Data Analytics & Reporting

We are seeking an AI Engineer – Decision Science with strong expertise in machine learning, artificial intelligence, and advanced analytics to design and deploy intelligent decision systems across risk domains. This role focuses on applying AI and ML to transform risk processes, including automated decisioning, sentiment analytics, and next-generation agentic AI solutions powered by large language models (LLMs).

The ideal candidate combines technical depth with business acumen and has hands-on experience delivering AI-driven solutions within financial services, particularly across credit risk, fraud, or enterprise risk analytics.

Key Responsibilities

AI & Decision Science Model Development

• Design and deploy AI/ML-driven decision science solutions to support enterprise risk use cases including credit adjudication, collections, loan review, and risk monitoring.
• Build automated decisioning frameworks to optimize labor-intensive processes and improve consistency, speed, and accuracy of risk decisions.
• Develop advanced analytics solutions including:
  • Sentiment analysis and behavioral modeling
  • Fraud detection and anomaly detection models
  • Risk scoring and early warning systems
• Apply modern ML techniques (e.g., gradient boosting, deep learning, NLP) to uncover patterns and generate actionable insights.

Agentic AI & LLM Integration

• Design and implement agentic AI workflows leveraging externally hosted or third-party LLMs.
• Build AI-powered tools for:
  • Automated documentation generation
  • Knowledge retrieval and decision support
  • Intelligent workflow automation
• Ensure solutions meet governance, security, explainability, and responsible AI standards.

Data Engineering & Advanced Analytics

• Process and analyze large, complex structured and unstructured datasets using Python, SQL, and SAS.
• Perform exploratory data analysis, feature engineering, and experimentation to support model development.
• Create scalable, reusable data pipelines and analytical workflows.
• Identify emerging risks, behavioral patterns, and anomalies through advanced statistical and machine learning methods.

Model Performance & Governance

• Conduct pre- and post-implementation model analysis to evaluate performance, stability, and business impact.
• Ensure models meet model risk management (MRM) standards including documentation, explainability, validation support, and audit readiness.
• Maintain clear documentation of data lineage, assumptions, and modeling methodologies.

Collaboration & Enablement

• Act as a trusted advisor providing technical expertise to stakeholders across Risk, Credit, Fraud, and Finance.
• Collaborate with cross-functional teams to integrate AI solutions into enterprise workflows.
• Influence stakeholders and communicate complex AI concepts in a clear, business-relevant way.
• Support development of analytics tools, frameworks, and internal training initiatives.

Qualifications

Required

• Master’s degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, or a related quantitative field.
• 3+ years of experience in machine learning, AI, or advanced analytics within financial services or risk environments.
• Hands-on experience building and deploying ML/AI models for credit risk, fraud, or enterprise risk use cases.
• Strong programming expertise in Python, SQL, and SAS.
• Experience with decision science frameworks or automated decision systems.
• Familiarity with LLMs and AI-based automation (e.g., NLP, agent-based workflows).
• Solid understanding of model governance, validation, and regulatory expectations.
• Experience working with large, complex datasets including both structured and unstructured data.

Preferred

• Experience with credit bureau data and credit adjudication or account management models.
• Exposure to agentic AI frameworks, prompt engineering, and LLM orchestration tools.
• Knowledge of risk, capital, or treasury management frameworks.
• Experience with data visualization tools (Power BI, Tableau, Spotfire).
• Familiarity with cloud platforms (AWS, Azure, GCP) or big data tools (Spark, Databricks).

Experience in fraud analytics, collections, or enterprise risk management.

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.
• Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus.
• Provides specialized consulting, analytical and technical support.
• Exercises judgment to identify, diagnose, and solve problems within given rules.
• Works independently and regularly handles non-routine situations.
• 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.

Qualifications:

Foundational level of proficiency:

• Regulatory capital and stress testing.
• Compliance and regulation.
• Machine learning.
• Learning Agility.
• Systems Thinking.

Intermediate level of proficiency:

• Model risk management.
• Data visualization.
• Data wrangling.
• Data preprocessing.
• Critical thinking.
• Driving Results.
• Verbal & written communication skills.
• Collaboration & team skills.
• Analytical and problem solving skills.
• Data driven decision making.

Advanced level of proficiency:

• Quantitative financial modeling.
• Computational thinking and programming.
• Typically between 5 - 7 years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
• Deep knowledge and technical proficiency gained through extensive education and business experience.

Salary

$80,000.00 - $175,000.00

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.