Business Data Analyst Artificial Intelligence (AI/ML)

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 • 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. • 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, and performance metrics (accuracy, precision, recall, etc.). • Develop user stories, epics, and AI use case documentation. • Data Analysis & Management • Analyze data sources to assess availability, quality, and readiness. • Evaluate data governance and compliance requirements. • Collaborate with data engineers to define data pipelines, cleaning and transformation needs. • Support creation of data dictionaries and metadata documentation. • Collaboration with AI/ML Teams • Work closely with Data Scientists, Machine Learning Engineers, and 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. • 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. • AI Governance, Ethics & Compliance • Ensure AI solutions comply with data privacy regulations (GDPR, CCPA). • Adhere to ethical AI principles (fairness, bias mitigation, transparency). • Assist in defining model explainability and auditability requirements. • Support risk management for AI-based decisions. • Process Integration & Automation • Integrate AI models into business workflows and applications. • Define target operating models (TOM) for AI adoption.

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