AI Engineer- Decision Science

Final date to receive applications:07/07/2026

Address:33 Dundas Street West Job Family Group:

Data Analytics & ReportingAI Engineer – Decision Science 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,…