Associate IT Business Analyst

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Job Title: Associate IT Business Analyst

Career Level: E

Introduction to role:

Be part of history in the making. AstraZeneca is building its firstever biologics manufacturing campus in Singaporea USD 1.5 billion stateoftheart hub that unites endtoend AntibodyDrug Conjugate (ADC) capabilities under one roof: smallmolecule chemical API production largemolecule antibody manufacturing conjugation and fillandfinish (including sterile filling and lyophilization). Powered by advanced digitalization automation and artificial intelligence for autonomous manufacturingand targeting carbon neutralitythis nextgeneration 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 deliveryshaping ideas into production solutions that improve quality safety and productivity

In this role you will collaborate with product leaders data scientists engineers and operational teams to pinpoint high-value opportunities. You will build compelling business justifications and speed up the development of intelligent systems and automated solutions from concept to scale. Your work will help reduce cycle times enhance decision quality and unlock efficiencies that ultimately support patients and the business. You will expand your skills in a culture that prizes curiosity and continuous learning with the backing to experiment iterate and deliver.

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 tracked and realized. This is a chance to build capabilities that endurewhile shaping your own development through hands-on outcomes-focused work.

Accountabilities:

• Discovery and Opportunity Shaping: Collaborate with partners to understand priority problems. Find and prioritize opportunities in machine intelligence and process automation that support strategy and responsible AI principles. Set guardrails and success metrics to maintain value and compliance.
• Translate complex business demands into clear analysis and solution builds. Evaluate data readiness. Share model results and change effects to support safer decisions and faster adoption.
• Collaborate with engineering and data science partners to develop demonstrations of value. Run rapid experiments and convert successful pilots into resilient production solutions.
• Value Cases and Benefits Realization: Build return on investment and benefits models with baselines and measurement plans; track improvements in productivity quality and safety post launch to evidence outcomes.
• Architecture and ML Ops Collaboration: Coordinate with platform integration data and observability teams; align to corporate architectural standards to improve process performance and digital maturity.
• Lead structured compose sessions to gather functional and technical requirements define data labeling needs and agree acceptance criteria.
• Analytics and Insights: Capture and analyze usage evolving data patterns and model-generated outcomes; support A/B testing; generate insights for iterative optimization and decisionmaking.
• Change and Adoption: Drive communications training and AI literacy; shape responsible use guidelines and humanintheloop models to secure adoption and sustained value.
• Planning and Delivery: Build coordinated plans and handle stage gates from discovery through deployment and hypercare encompassing data integration model verification and launch readiness.
• Collaborator Management and Governance: Map customers; run clear communications and status for executives and teams; surface AI risks performance and benefits; establish and run working groups and steering forums; ensure compliance with GxP privacy cybersecurity ethical AI audit and model governance with evidence and explain ability artifacts.
• Handle a clear and verifiable scope along with change management that covers retraining drift management and feature updates. Oversee RAID to maintain timelines across data sources model pipelines and integration layers.
• Financial Stewardship: Develop and lead all aspects of budgets forecasts and actuals covering costs associated with cloud resource consumption AI service charges 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.
• Keep accurate metrics and dashboards. Provide clear updates to governance bodies. Support data-driven decisions through self-service analytics and AI-assisted reporting. Develop and refine Agile hybrid or waterfall approaches using innovation sprints labs and creative thinking.
• Site and Global Coordination: Align site execution to global standards and frameworks; lead all aspects of localization while preserving platform guardrails.

Essential Skills/Experience:

• 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 technology-based Solution Composition: Translate sophisticated business requirements into clear actionable analysis solution compositions and decisions. Communicate model performance data readiness and change implications to address business risks and issues.
• Innovation Pipeline Contribution: Give 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 tracking for AI-enabled initiatives that meet agreed business outcomes.
• Architecture Data and ML Ops Collaboration: Coordinate with IT capability teams to identify crucial capabilities (Data platform Integration patterns ML Ops 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 Capture 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: Actively 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 manage stage gates from discovery to deployment and hyper care including data onboarding model validation and launch.
• Partner Engagement: Map collaborators run clear comms and status reporting for execs and teams surface AI risks/performance/benefits and facilitate decisions.
• Scope & Change Control: Maintain a clear testable scope; run CRs; assess time/cost/quality impacts including model retraining drift management and feature updates.
• Governance & Compliance: Establish and run project governance routines (steerco working groups). Ensure adherence to quality GxP data privacy cybersecurity ethical AI model governance audit requirements; maintain proof of compliance and explain ability 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 & Cutover: Manage readiness transition activities hyper care training and handover to BAU including ML Ops handover monitoring and model lifecycle management.
• Maintain accurate project metrics and dashboards. Provide timely transparent reports to governance bodies. Use interactive analytics platforms and automated report generation to advise decisions.
• 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 & Global Coordination: Align site-level execution with global standards and architectures; handle localization while preserving enterprise consistency and AI platform guardrails.

Desirable Skills/Experience:

• Experience in life sciences healthcare or other regulated environments with familiarity applying model governance and explain ability in practice
• Exposure to modern cloud and data platforms (e.g. Azure AWS or GCP) Databricks or similar and observability tools for data and ML pipelines
• Solid understanding of SQL and/or Python for exploratory analysis and validating data readiness
• Experience with large language models prompt engineering and retrievalaugmented generation in enterprise settings
• Proficiency with split testing frameworks product analytics and experimentation develop
• Strong data storytelling and visualization skills using tools such as Power BI or Tableau
• Certifications such as CBAP PMIPBA Scrum/SAFe cloud practitioner/architect or AI/ML specialty
• Practical understanding of global data privacy regulations (e.g. GDPR) and cybersecurity in AI-enabled solutions
• Financial modelling experience for return on investment and benefits tracking including cloud consumption forecasting
• Ability to coordinate across global teams and time zones balancing local needs with enterprise standards

When we put unexpected teams in the same room we ignite ambitious thinking with the power to

Inspire life-changing -person working gives us the platform we need to connect work at pace and challenge

perceptions. Thats why we work on average a minimum of three days per week from the office. But that

Doesnt mean were not flexible. We balance the expectation of being in the office while respecting individual

flexibility. Join us in our unique and ambitious world.

Why AstraZeneca:

Here technology and science meet at scale to improve patients lives. You will work with unexpected teams that fuel ambitious ideas experiment with groundbreaking tools and turn them into real outcomes for colleagues and patients. We back ambition with investment and guardrails that make responsible AI the default and we value courtesy and learning alongside high performance. You will grow through coaching feedback and handson challengeswhether thats a hackathon prototype or deploying a production model that shortens time to critical decisions.

Call to Action:

If youre ready to shape highimpact AI and automation from discovery to production while accelerating your own development step forward and help us turn possibility into measurable value today!

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds with as wide a range of perspectives as possible and harnessing industry-leading skills. We believe that the more inclusive we are the better our work will be. We welcome and consider applications to join our team from all qualified candidates regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment) as well as work authorization and employment eligibility verification requirements.