AI Engineer- Decision Science

BMO•Toronto, ON

12d•CA$82,800 - CA$154,800•Onsite

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.