Associate Director, Clinical AI Translation & Solutions

AstraZenecaCambridge, Massachusetts, United States of AmericaonsiteSalary not postedView the original posting at AstraZeneca ↗

Job Title: Associate Director, Clinical AI Translation & Solutions Introduction to role: About AISI AI Science & Innovation (AISI) sits at the centre of AstraZeneca’s R&D AI transformation within Enterprise AI Unit (EAI). Our remit is to build, buy, and deliver the AI models and agents that change pipeline outcomes across discovery, translational science, biomarkers, and clinical development, ultimately improve patients’ lives. The opportunity We are building an end-to-end Enterprise AI engine that unites data foundations, AI models, platforms, and business-facing applications to accelerate results across the value chain. Success comes from reusing what already works, sharing ideas across teams, and scaling impact rather than building in isolation. Role overview AstraZeneca is building an AI capability for Clinical Development that will improve how trials are designed, conducted, monitored, and analysed across Enterprise R&D Functions. We are hiring an Associate Director, Clinical AI Translation & Solutions to turn complex clinical-operational workflows into useful, measurable, and reusable AI solutions. The role sits in the Clinical AI Applied Science & Translation team and engages directly with clinical stakeholders, Engineering, and data teams to shape priorities, requirements, and delivery through AstraZeneca’s existing Engineering organization. This is a hands-on AI research and development role with a focus on applied-science and translation in the clinical space. You will work directly with clinical operations teams, CRAs, medical monitors, data owners, quality partners, and engineers to understand how work is actually performed, identify where AI can safely help, and translate that understanding into working software. You will prototype in code, make architecture and scope trade-offs with Engineering, and remain accountable through evaluation, deployment, adoption, and iteration. The initial focus will include agentic workflows for improving clinical operations, with the expectation that reusable patterns will scale across Clinical Development. The strongest candidate combines strong software and applied-AI judgment with the credibility and empathy to work alongside clinical professionals in a regulated environment. Accountabilities: Own a portfolio of clinical AI workflows from discovery through deployment, adoption, and measurable benefit realization. Observe workflows directly alongside CRAs, site-monitoring leaders, clinical operations, and medical stakeholders, converting pain points into clear product requirements, data contracts, acceptance criteria, and evaluation plans. Build and iterate hands-on prototypes in Python and related technologies; contribute production code, review pull requests, inspect logs, reproduce failures, and work directly with engineers to resolve them. Design agentic workflows that combine language models, deterministic services, enterprise tools, and human review, with clear evidence provenance, uncertainty handling, and fail-safe behaviour. Make disciplined scope decisions that separate demonstrations from production releases and non-GxP workflows from regulated use. Define outcome measures spanning technical performance, clinical usefulness, workflow quality, user adoption, cycle time, and operational impact. Lead human-in-the-loop testing with clinical subject-matter experts and translate feedback into prioritized, testable product changes. Work with data-product owners and platform teams to establish reliable access to clinical systems, documents, and structured data while protecting privacy and data integrity. Partner with Quality, Regulatory, Privacy, Security, and R&D IT to embed GCP, GxP, validation, auditability, and lifecycle requirements from the outset. Promote reuse of enterprise platforms, data products, agent components, and evaluation assets; identify when to build, buy, partner, or adapt an existing capability. Communicate product decisions, evidence, risks, dependencies

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