Business Data Analyst Artificial Intelligence (AIML)

Job Title: Senior Business Analyst Artificial Intelligence (AIML)
Location: Toronto ON
Work Style: Hybrid (2 days onsite preferred)

Skills: Business Analysis Digital : Machine Learning Digital : Artificial Intelligence(AI)
Experience: 8-10 Years

We are seeking a highly experienced AI-focused Business Analyst to drive the identification analysis and implementation of Artificial Intelligence and Machine Learning solutions across the organization. This role bridges the gap between business objectives and data science/engineering teams ensuring AI initiatives deliver measurable business value ethical compliance and scalability.

Key Responsibilities
1. Business Strategy & AI Opportunity Identification
Identify and evaluate AI/ML use cases aligned with business goals (e.g. predictive analytics NLP computer vision).
Collaborate with leadership to define AI strategy and roadmap.
Conduct feasibility analysis including cost-benefit ROI and risk assessment.
Recommend AI-driven solutions to improve efficiency decision-making and customer experience.

2. Requirements Engineering for AI Solutions
Elicit and document AI-specific functional and non-functional requirements.
Translate business problems into data science problems and hypotheses.
Define:
Model inputs/outputs
Training data requirements
Performance metrics (accuracy precision recall etc.)
Develop user stories epics and AI use case documentation.

3. Data Analysis & Management
Analyze data sources to assess:
Data availability quality and readiness
Data governance and compliance requirements
Collaborate with data engineers to define:
Data pipelines
Data cleaning and transformation needs
Support creation of data dictionaries and metadata documentation.

4. Collaboration with AI/ML Teams
Work closely with:
Data Scientists
Machine Learning Engineers
Data Engineers
Facilitate understanding of business context for model development.
Ensure models align with business needs and operational constraints.
Participate in model evaluation validation and performance monitoring discussions.

5. AI Model Lifecycle Support
Support end-to-end lifecycle:
Problem definition
Model development
Testing & validation
Deployment & monitoring
Define acceptance criteria for AI models.
Ensure continuous improvement using feedback loops and retraining strategies.

6. AI Governance Ethics & Compliance
Ensure AI solutions comply with:
Data privacy regulations (GDPR CCPA)
Ethical AI principles (fairness bias mitigation transparency)
Assist in defining model explainability and auditability requirements.
Support risk management for AI-based decisions.

7. Process Integration & Automation
Integrate AI models into business workflows and applications.
Define target operating models (TOM) for AI adoption.
Work with product