Jobs at AstraZeneca

Supports AI project coordination, testing, user acceptance testing, documentation, training materials, stakeholder communication, launch readiness, and user feedback activities. The intern will collaborate with technical and business teams while gaining experience in AI implementation, responsible AI delivery, project coordination, and adoption of AI-enabled solutions.

Lead commercial decision science projects for a rare disease business, developing predictive and machine learning models using healthcare and real-world data. Apply statistical modeling, experimental design, causal inference, survival analysis, forecasting, and AI-assisted analytics. Translate findings into commercial recommendations while ensuring privacy, explainability, fairness, validation, monitoring, and governance. Partner with cross-functional teams, manage project milestones, communicate insights to technical and non-technical stakeholders, and coach peers.

Support learning and development initiatives for AstraZeneca’s Oncology R&D organization. Responsibilities include identifying learning needs, coordinating projects and timelines, researching learning trends, supporting Degreed, Cornerstone, and Xyleme platforms, creating communications and learning resources, analyzing learning data, and exploring responsible AI applications in learning. The intern will gain practical experience in a global pharmaceutical R&D environment while working with L&D stakeholders.

Leads financial oversight and business partnering for Oncology therapeutic areas, owning P&L performance, gross-to-net stewardship, budgeting, forecasting, month-end reporting, resource allocation, and commercial analytics. The role provides strategic financial insights, evaluates business cases, manages risks and opportunities, strengthens controls and audit readiness, and partners with senior leaders to improve investment decisions and financial processes.

Design, build, and operate AWS cloud platforms supporting production generative, agentic, and conversational AI workloads. Develop reusable AWS CDK infrastructure in TypeScript or Python, containerized ECS workloads, CI/CD and GitOps workflows, AI gateways, observability, security, governance, reliability, and cost optimization capabilities. Partner with AI, data, software, and security teams to transition prototypes into resilient production services within a regulated pharmaceutical environment.