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About our Team: Predictive AI and Data team   is responsible for   providing AI and Bioinformatics solutions to the scientists across the spectrum of drug development and discovery in AstraZeneca (both pre-clinical and clinical stages). The primary aim is to find ways to accelerate the drug development process by   leveraging   existing company data in combination with the most   cutting-edge   AI approaches in day-to-day scientific work across the company. Introduction to role: As   Associate Director ,   OmicsAI , you will   be   accountable for designing and deploying advanced Machine Learning and AI solutions used for processing,   analysis   and interpretation of   multi-omics data ( transcriptomics ,   proteomics   and cell painting) within toxicological safety domains. You will then help build out the robust solution in production in collaboration with other data science and software developer teams across European and Asian R&D hubs.   This is a   primarily a   hands-on role with   additional   responsibilities   for choosing strategic direction and   technical leadership when working in a larger team. Accountabilities: • Collaborate with scientists from across the company to understand their challenges and work with them to build the platform that underpins their research. • Take responsibility for designing, and deploying machine learning models for a large-scale analysis of clinical transcriptomics , proteomics, and cell painting   data • Design and build machine learning models for transcriptomics ,   proteomics   and cell painting   data to predict the risk of clinical events, patient segmentation, or for high-throughput compound screening • Apply a range of data science methodologies, developing novel data science solutions where off-the-shelf methodologies do not fit • Lead the development, and implementation of machine vision models in cell painting and advanced cell   models   space: from project ideation, through PoC to deployment in production in collaboration with colleagues from R&D IT • Build and manage effective relationships with stakeholders to ensure   utilization   and value of information resources and services. Clearly and objectively communicate results, as well as their associated uncertainties and limitations to shape solutions • Work effectively across several   timezones   with AI research teams in China, India ,   Europe   and the US East Coast , communicating the requirements for the AI models and evaluating the available solutions • Champion a “production first attitude” to ensure the necessary infrastructure and platforms are available to scale exploratory research to production. • Be a part of a hard-working team, continuously improving AstraZeneca’s Machine Learning development environments, platforms, and tooling. • Work closely and collaboratively with internal governance and compliance functions such as   Cyber Security and Data Privacy to secure   the computing environment   without obstructing end-user productivity. Essential Skills/Experience: • PhD   (or equivalent years of experience) in mathematics, computer science, engineering, physics, statistics, computational   sciences   or a related field. • Proven experience in applying   machine vision models   in   healthcare/pharmaceutical research or biotech • Demonstrated   experience in implementing machine learning/AI workflows to automate bioinformatics analysis pipelines, ideally in the Pharma and/or Healthcare space • Familiarity with modern foundation models for transcriptomics or Cell Painting data   (e.g.,   Geneformer ,   scGPT ,   scFoundation   etc.) • In-depth experience in modelling complex   imaging   datasets   ( e.g.,   H&E, cell painting, etc.) • Experience building ML/AI models to predict compound safety • Experience of manipulating and analysing large high dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results across stakeholders • Familiarity with existing machine vision models: CNNs, vision transformers, diffusion models etc. for self-supervised and multimodal training (e.g.,   ResNet ,   UNet , DINO, CLIP, Stable Diffusion) • Advanced skills in programming languages such as Python, and experience with AI libraries and frameworks (e.g., TensorFlow,   PyTorch ). • Familiarity with GitHub,   CI/CD pipeline , and best DevOps and   MLOps   practices • Track record of publications in top AI conferences or journals in pharmaceutical research (e.g.,   NeurIPS , ICML, Nature Machine Intelligence, Nature Communications, NEJM AI, etc.) • Strong knowledge of software development and machine learning deployment principles • Demonstratable experience working with AWS or a similar cloud environment • Excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical partners. • Strong leadership and project management skills, with   a track record   of leading successful bioinformatics projects • Knowledge of AI ethics and responsible AI practices Desirable Skills/Experience : • Experience working with proteomics datasets • Experience building vision models using Cell Painting images • Experience building ML/AI models to predict compound safety • Experience in a complex global organization. • Experience using DevOps and   MLOps   to enable automation strategies • Experience working in an Agile team with knowledge or experience of working in product or platform-focused delivery Date Posted 23-4月-2026 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.