Associate Director, Translational Data Strategist – Bioinformatics & AI Lead, TICA

AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development, and commercialisation of prescription medicines for some of the world’s most serious diseases. At AstraZeneca, we are pioneering new frontiers by identifying and treating patients earlier, working towards the aim of eliminating cancer as a cause of death.

At AstraZeneca, we work together across global boundaries to make an impact and find answers to challenges. We do this with the utmost integrity even in the most difficult situations, because we are committed to doing the right thing. We continuously forge partnerships that help pursue world-class medicines in new ways, combining our people’s exceptional skills with those of people from all over the globe.     

Be part of fulfilling our ambition to be world leaders in Oncology. We are already the fastest growing team within AstraZeneca and across the industry, and there are countless new indications and targets in our game-changing pipeline. The vision of AstraZeneca Oncology is to redefine cancer, redefine our solutions to cancer, and restore patients' lives. To achieve this, we have an exciting and diverse portfolio that focuses on four scientific platforms that we believe show the greatest potential for yielding effective medicines for cancer patients. A place built on courage, curiosity and collaboration – we make bold decisions driven by patient outcomes. Empowered to lead at every level, free to ask questions and take smart risks that write the next chapter for our pipeline.

We are seeking a postdoctoral scientist with a background in bioinformatics and artificial intelligence (AI)to join our world class centre for discovery and development of innovative cancer medicines. As a member of the Tumour & Immune Cell Atlas(TICA) team, in Translational Medicine Oncology R&D,you will be responsible for guiding biomarker-driven translational research and the strategic implementation of AI. You will partner closely with translational scientists, oncology data science and AI colleagues to deliver innovative solutions that guide indication selectioninsights, identify targeted patient populations, support rational drug combinations and inform mechanisms of resistance.

Key Responsibilities and Accountabilities:

• Accountable for the delivery of robust, scientifically driven biomarker data to guide indication selection, identify targeted patient populations, and enable biomarker-driven clinical studies within the TICA portfolio.

• Develop novel metrics and analytical approaches to uncover, design, and visualize meaningful patterns within complex biomedical datasets.

• Deliver analytical support for tumour council meetings and translational data review processes, ensuring actionable scientific insights.

• Present scientific progress to Translational Medicine leadership, governance boards, and project teams; share reproducible code and train peers on critical tools for data interpretation.

• Champion new bioinformatics technologies and methodologies, continuously assessing and improving existing processes to support the department’s strategic objectives.

• Collaborate with ODSAI (Oncology Data Science and AI) on the design, implementation, and validation of machine learning and deep learning models to analyse TICA omics, imaging, and clinical data, supporting biomarker discovery and patient stratification.

• Drive the development and execution of AI strategy for translational research, working with stakeholders to align AI initiatives with TICA objectives.

• Work closely with computational scientists, data engineers, software developers, and translational researchers to co-create AI initiatives and accelerate data-driven breakthroughs.

Essential Requirements and Qualifications

Education Required:

• PhD in a scientific discipline relevant to oncology.

Experience Required:

• Demonstrated proficiency in bioinformatics workflows and data analysis, including the use of programming languages such as Python and R.

• Track record of integrating omics, imaging, and clinical data to deliver translational insights.

• Significant experience developing and applying machine learning and AI algorithms to biological/clinical datasets; experience with deep learning frameworks is desirable.

• Proven communication, presentation and influencing skills.

• Experience of drug discovery or development and/or exposure to translational research desirable.