AI Engineer- Decision Science
$82,800–$154,800 year
On-site · Toronto, Ontario, Canada
Job Summary
AI Engineer – Decision Science responsible for designing and deploying AI/ML-driven decision science solutions to support enterprise risk use cases (credit adjudication, collections, loan review, risk monitoring). Build automated decisioning frameworks to improve speed and consistency of risk decisions; develop analytics such as sentiment analysis, behavioral modeling, fraud/anomaly detection, risk scoring and early warning systems. Integrate agentic AI workflows leveraging external LLMs; create AI-powered tools for automated documentation, knowledge retrieval, and intelligent workflow automation. Ensure governance, security, explainability, and responsible AI standards. Process and analyze large structured and unstructured datasets using Python, SQL, and SAS; create scalable data pipelines; identify emerging risks. Conduct model performance assessment and align with model risk management standards; provide technical guidance to Risk, Credit, Fraud, and Finance stakeholders; collaborate to integrate AI solutions into enterprise workflows; document data flow and methodologies. Attributes such as independent problem solving, analytical thinking, and communication skills are emphasized; the role is based in Toronto with full-time employment for BMO Financial Group.
Required Qualifications
- 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
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