Associate Director, Computational Biology, Oncology Data Science, Oncology R&D

May 26, 2023
Gaithersburg - MD , Waltham - MA
Job Type
Work Setting


The Computational Biology group drives high-impact cancer research projects and delivers data-driven, actionable insights through the application of computational science to all areas of AstraZeneca’s Oncology portfolio. The group is embedded in the Oncology Data Science department and has extensive expertise and has a track record in computational oncology and data science, undertaking both portfolio applied informatics research and the development of cutting edge capabilities.

AstraZeneca is a company that always follows the science and turns ideas into life changing medicines. Oncology Data Science plays a unique role in driving discovery and translation by leading the adoption of computational/data driven approaches to all aspects of the drug discovery process. If this sounds like a role you are excited about, AstraZeneca might be the place for you!

Roles and Responsibilities

The Computational Biology team is undergoing expansion to meet the strategic opportunities at AZ Oncology. We are looking for an Associate Director of Computational Oncology to lead our efforts of deriving integrated predictive models for the understanding of the biological mechanisms of response and resistance to treatment, across therapy areas using ex vivo patient multi-omic data. This is a rare opportunity to lead and develop our strategic approaches in the cross-sections between computational biology, discovery sciences and translational oncology in a highly matrixed collaborative project. The ideal candidate will have proven academic or industry experience in the computational oncology field with an outstanding track record leading independent programs. The successful execution of this role will impact the wider AZ oncology community, and our patients through discovery of biomarkers to inform drug combination and patient selection strategies, discovery of potential novel oncology targets and further our knowledge of cancer biology and evolution. To do this you will:

  • Develop the computational strategy, deliver as an individual contributor and lead a matrixed team of highly qualified computational scientists to deliver integrated predictive models using preclinical, ex-vivo and clinical multi-omic data
  • Partner closely with leaders across Translational Medicine and Bioscience to establish a data science strategy that facilitates back-translation by discovery of novel target and/or prediction of drug combinations.
  • Use your expertise in cancer biology/drug discovery to deliver actionable insights that impact the development of the next generation of cancer medicines.
  • Form effective collaborations with industry and academic leaders in the field, to develop AZ’s IP and/or publish AZ’s work in high impact journals.

Essential requirements for the role

  • Relevant PhD (or equivalent graduate degree plus proven applied experience), combining technical expertise in computational/systems biology, bioinformatics or applied biostatistics.
  • Expertise in mathematical modelling of preclinical cancer perturbation data
  • Significant experience in genomic biomarker analyses in oncology clinical studies
  • Deep knowledge of cancer genetics and algorithmic and statistical methods applicable to cancer genomics.
  • Highly attuned communication skills and extensive experience of working as part of a team or a matrix team in coordinating efforts and responsibilities of project goals.
  • The ability to coordinate and pursue multiple simultaneous projects to tight deadlines.
  • R and/or Python programming expertise in a Unix environment making use of high-performance computing environments.
  • Experience of graph modelling, machine learning, artificial-intelligence, Bayesian analytics or other non-traditional approaches to model biological data.

Desirable requirements for the role

  • Outstanding publication record.
  • Well connected to a wide network of bioinformatics and oncology communities.
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