IT Business Analyst

Job Title: IT Business Analyst Career Level: E Introduction to role: Be part of history in the making. AstraZeneca is building its first‑ever biologics manufacturing campus in Singapore—a USD 1.5 billion, innovative hub that unites end‑to‑end Antibody–Drug Conjugate (ADC) capabilities under one roof: small‑molecule chemical API production, large‑molecule antibody manufacturing, conjugation, and fill‑and‑finish (including sterile filling and lyophilization). Powered by sophisticated digitalization, automation, and artificial intelligence for autonomous manufacturing—and targeting carbon neutrality—this next‑generation site will set a new benchmark for environmentally responsible biologics production. Are you ready to turn data, cognitive technologies, and robotic processes into measurable business outcomes that help deliver life-changing medicines? Do you thrive at the intersection of discovery and delivery—shaping ideas into production solutions that improve quality, safety and productivity? This role combines IT Business Analysis and Project Management skills to find AI/ML and automation opportunities. It develops data and AI-based solutions and delivers them through all stages, from initial exploration to production and ongoing support. This role will collaborate with senior business partners, IT collaborators, and regional partners to develop arguments, assist in business process design, and ensure that proposed solutions meet our quality and compliance standards—while identifying, piloting, and scaling AI/automation opportunities that improve outcomes safely and responsibly. The role defines benefits cases and KPIs, governs scope, risk, budget, and compliance (GxP, privacy, ethical AI), and drives collaborator engagement and change adoption. It ensures measurable outcomes via focused planning, visible reporting, and continuous optimization of AI solution performance and business value. From day one, you will lead discovery, structure experiments, and orchestrate the path to production with strong governance. You will help set guardrails for responsible AI, support operational frameworks involving human participation, and establish clear metrics so benefits are supervised and realized. This is a chance to build capabilities that endure—while shaping your own development through hands-on, outcomes-focused work. Accountabilities: Business Discovery & AI Opportunity Identification: Engage collaborators to understand and prioritize business needs to drive operational efficiencies. Proactively identify AI/ML and automation use cases aligned to strategic objectives and ethical AI principles. Data- and AI-Driven Solution Design: Translate sophisticated business requirements into clear, actionable analysis, solution designs, and decisions. Communicate model performance, data readiness, and change implications to address business risks and issues. Innovation Pipeline Contribution: Supply innovative ideas and aligned with AZ IT strategy. Collaborate with AZ IT partners to incubate proofs of value, run rapid experiments, and transition successful pilots into production. Value Cases & Benefits Realization: Develop and shape cases including return on investment, productivity uplift, quality/safety improvements, and benefits supervising for AI-enabled initiatives that meet agreed business outcomes. Architecture, Data, and MLOps Collaboration: Coordinate with IT capability teams to identify crucial capabilities (Data platform, Integration patterns, MLOps ,Observability) required for successful outcomes. Align solutions to enterprise patterns and improve process performance and digital maturity. Design Workshops & Experimentation: Lead workshops to elicit functional and technical requirements, label data needs, define guardrails, and agree on successful metrics for AI solutions. Analytics & Insights Collection and analyse information from relevant sources to report data trends and model insights, enabling informed decision-making and continuous improvement. Support A/B testing and iterative optimization. Change & Adoption : Optimally engage collaborators and share knowledge with delivery teams. Support communication plans and organizational change for projects, including AI literacy, responsible use guidelines, and human-in-the-loop operating models. Planning & Delivery: Develop integrated schedules and handle stage gates from discovery to deployment and hyper care, including data onboarding, model validation, and launch. Governance & Compliance: Establish and run project governance routines (steerco, working groups). Ensure consistency to quality, GxP, data privacy cybersecurity, ethical AI, model governance, audit requirements; maintain proof of compliance and explainability documentation. Risk/Issue/Dependency Management: Maintain RAID logs; quantify impact, drive mitigations. Handle dependencies across data sources, model pipelines, and integration layers to protect timelines. Budget & Financials: Create and handle budgets, forecasts, and actuals to keep project within budget including cloud consumption, AI service cost, and licensing. Operational Readiness and Cutover: Orchestrate readiness, cutover, hypercare, training and transition to BAU or an equivalent level of experience with ML Ops transfer, oversight and lifecycle management. Data & Reporting Cadence :Maintain accurate project metrics and dashboards; provide timely, transparent reporting to governance bodies; use data to advise decisions, demonstrating self-service analytics and AI-assisted reporting. Methodology & Ways of Working :Apply appropriate delivery approach (Agile, hybrid, or waterfall). Ensure ceremonies/cadence and continuously improve processes with innovation sprints, labs, and Design Thinking practices. Security & Privacy by Design: Embed security requirements; conduct risk assessments; ensure data classification and privacy controls from the outset, including PII handling, model security, adversarial robustness, and responsible data use. Site and Global Coordination: Align site execution to global standards and frameworks; lead all aspects of localization while preserving platform guardrails. Essential Skills/Experience: Minimum 12 years in biopharma/pharma manufacturing with specialised domain expertise across enterprise systems including but not limited to (SAP ECC/S/4HANA), manufacturing systems (PAS‑X, PI Historian, Track SYS), multivariate analysis (SIMCA Online, SIPAT, Pharma MV), and lab systems (LIMS, OneLab, MODA, Empower) Proven Business Analysis and Project Management delivery in fast-paced environments, including AI/automation initiatives; adept at navigating sophisticated integrated systems, data platforms, APIs, and analytics/ML workflows Domain Expertise: Biopharma/pharma manufacturing; enterprise, manufacturing, multivariate, and lab systems Business Analysis & PM Delivery: End-to-end change delivery, incl. AI/automation initiatives Data & Integration: Data platforms, APIs, analytics/ML workflows; sophisticated system navigation Collaborator Engagement: Map collaborators, run clear comms and status reporting for execs and teams, surface AI risks/performance/benefits, and facilitate decisions. Regulatory Compliance: GxP, GDPR, model governance, audit-ability Virtual Collaboration: Lead cross-functional AI/innovation squads Vendor and Third-Party Supplier Management: AI tech providers, cloud platforms, system integrator Desirable Skills/Experience: Degree in releva