Director / Associate Director, Translational Data Science, Haematology R&D

About the Team

The data science group within Hematology Translational Medicine is leading the application of advanced analytics and artificial intelligence to clinical and multi-omics data, aiming to drive data-informed clinical development and discovery in hematology. Our team empowers cross-functional teams by transforming complex datasets into actionable insights across key areas, including T-cell engagers (TCEs), CAR-T cell therapies, small molecules, and ADCs. By doing so, we shape strategies for clinical trial design, patient selection, and the advancement of personalized medicine.

The Opportunity

We seek a highly skilled and visionary Director or Associate Director to manage the hematology data science team, responsible for shaping and executing the AI and data science strategy within our hematology pipeline. The ideal candidate combines technical mastery in machine learning and foundation models with deep experience in the analysis of clinical and molecular data from hematologic malignancy studies. Your expertise will critically inform decisions from early-phase trials to registration studies.

You will own the integration of novel data modalities across including genomic testing, ctDNA, and MRD, develop and apply state-of-the-art AI and foundation models to large and complex clinical datasets, and serve as an ambassador both inside and outside the organization.

Key Responsibilities

• Manageateam who are developing, applying, and operationalizing statistical and AI-driven models across clinical and real-world datasets in hematologic malignancies. 

• Work cross-functionally to deliver data that impacts decision making across ourAstraZeneca Heme portfolio. 

• Devise strategies for integrating and interpreting multimodal datasets to support our goal of making all data computable (clinical, genomic, transcriptomic, proteomic, imaging, and liquid biopsy) to enhance understanding of patient response/resistance and biomarker development in hematology for assets, especially TCE and CAR-T. 

• Oversee and contribute to the application of foundation models (including transformers, LLMs, and multimodal AI) to support biomarker discovery, response prediction, and clinical trial optimization. 

• Foster strong cross-functional partnerships with translational medicine, clinical development, and biostatistics to ensure data-driven approaches inform trial design, patient stratification, and asset development. 

• Continuously innovate analytical workflows for large-scale, high-dimensional clinical and molecular data, ensuring analytical rigor, reproducibility, and interpretability. 

• Mentor and develop a high-performing data science team, cultivating excellence in scientific communication and collaborative problem-solving. 

• Publish research in high-impact journals and represent AstraZeneca at key scientific meetings.

Essential Skills & Experience

• PhD (or equivalent) in computational biology, bioinformatics, computer science, biostatistics, or closely related field, plus industry experience (Director 6+ years industry experience; Associate Director, 4+ years industry experience).

• Demonstrable expertise applying machine learning (including deep learning and foundation models) to clinical, genomic, and/or multi-omics datasets, ideally in hematologic malignancies. 

• Proven leadership analyzing and interpreting datasets generated from T-cell engagers (TCE), CAR-T therapies, and liquid biopsy (ctDNA/MRD) platforms. 

• Strong understanding of the clinical landscape and biology of hematologic cancers, especially regarding patient stratification, biomarker development, and therapeutic response/resistance mechanisms. 

• High proficiency in statistical programming (e.g., R, Python) and experience leveraging high-performance and cloud computing environments. 

• Exceptional communication skills, with an ability to explain complex analytical findings to scientific, clinical, and business leaders. 

• Track record of high-impact publications and/or open-source contributions in computational biology, AI, or oncology. 

• Experience directly leading teams, inspiring collaboration, and executing on a vision in a fast-paced environment.

Preferred Qualifications

• Demonstrated experience or strong understanding of GCP-compliant practices for data analysis, including the application of version control, audit trails, and standardized documentation in environments such as Python and R/RStudio.

• Demonstrated success in implementing integrated genomics and imaging datasets to achieve clinical value using AI.

• Experience with Agentic AI and desire to incorporate it into AZ workflows

• Knowledge of current best practices and emerging technologies for MRD and ctDNA analysis. 

• Familiarity with regulatory and clinical operations aspects of late-stage hematology trials. 

• Deep connections in the data science/AI and hematology communities through engagement in conferences, consortiums, or collaborative research.