
Director / Associate Director, Translational Data Science, Haematology R&D
- Waltham, MA
- Permanent
- Full-time
- Manage a team 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 our AstraZeneca 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.
- 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.