Business Analyst

• Job Title: Senior Business Analyst – Artificial Intelligence (AIML)
• Work Assignment Location: Toronto, ON (Hybrid)
• Duration: 6 Months Contract with possible extension

• Job Descriptio
• nWe 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 Responsibiliti

• es
• 1. Business Strategy & AI Opportunity Identificat
• ionIdentify and evaluate AI/ML use cases aligned with business goals (e.g., predictive analytics, NLP, computer visio
• n).Collaborate with leadership to define AI strategy and roadm
• ap.Conduct feasibility analysis including cost-benefit, ROI, and risk assessme
• nt.Recommend AI-driven solutions to improve efficiency, decision-making, and customer experien

• ce.
• 2. Requirements Engineering for AI Solut
• ionsElicit and document AI-specific functional and non-functional requireme
• nts.Translate business problems into data science problems and hypothe
• ses.Def
• ine:Model inputs/out
• putsTraining data requirem
• entsPerformance metrics (accuracy, precision, recall, e
• tc.)Develop user stories, epics, and AI use case documentat

• ion.
• 3. Data Analysis & Manag
• ementAnalyze data sources to as
• sess:Data availability, quality, and read
• inessData governance and compliance require
• mentsCollaborate with data engineers to de
• fine:Data pipe
• linesData cleaning and transformation
• needsSupport creation of data dictionaries and metadata documenta

• tion.
• 4. Collaboration with AI/ML
• TeamsWork closely
• with:Data Scie
• ntistsMachine Learning Eng
• ineersData Eng
• ineersFacilitate understanding of business context for model develo
• pment.Ensure models align with business needs and operational constr
• aints.Participate in model evaluation, validation, and performance monitoring discus

• sions.
• 5. AI Model Lifecycle
• SupportSupport end-to-end lif
• ecycle:Problem def
• initionModel deve
• lopmentTesting & val
• idationDeployment & mon
• itoringDefine acceptance criteria for AI
• models.Ensure continuous improvement using feedback loops and retraining stra

• tegies.
• 6. AI Governance, Ethics & Co
• mplianceEnsure AI solutions comp
• ly with:Data privacy regulations (GDP
• R, CCPA)Ethical AI principles (fairness, bias mitigation, trans
• parency)Assist in defining model explainability and auditability requi
• rements.Support risk management for AI-based de

• cisions.
• 7. Process Integration & A
• utomationIntegrate AI models into business workflows and appl
• ications.Define target operating models (TOM) for AI
• adoption.Work wit

h product