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AstraZeneca

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Scientist, Data Science

🗓️ Posted 2026-08-17 US - Waltham - MA Full-time Hybrid Science Technology

Company shared salary

7,584 GBP–11,376 GBP/mo (91,009 GBP–136,513 GBP/yr)

Market rate

8,000 GBP–12,500 GBP/mo (96,000 GBP–150,000 GBP/yr)

Based on similar roles (title + domain + location).

About the Company

We are seeking a highly motivated Scientist to join a newly formed, dynamic team within early oncology R&D . The successful candidate will leverage their data science expertise in mining large datasets to drive our efforts in target identification, mechanism of action (MOA) studies, and biomarker strategy development , with a particular focus on analys e s related to the function and aging of the immune system . At AstraZeneca, you'll have the opportunity to make a significant impact on the future of healthcare while working in a collaborative environment at the cutting edge of research. The ideal candidate will thrive in this setting, contributing to our growth trajectory as we build our evolving team. Key Responsibilities: Execute and Maintain Pipelines: Process and analyze large-scale biobank datasets , human population data, and in-vitro biological data using established analysis pipelines. Analytical Support: Apply analytical methods and machine learning algorithms to help identify potential therapeutic targets and biomarkers. Cross-Functional Collaboration: Partner with wet-lab scientists to analyze experimental results for target identification and Mechanism of Action (MOA) studies. Data Visualization: Generate high-quality visualizations and reports to communicate findings to the project team. Strategic Contribution: Provide high-quality data and computational insights that contribute to the development of biomarker strategies. T

Requirements

  • ● Data Experience: Minimum 2 years of experience working with large-scale biological or population datasets , preferably including experience analyzing immune system aging/ function within the context of human and /or mouse data .
  • ● Coding Proficiency: Strong proficiency in Python or R .
  • ● Technical Knowledge: Solid understanding of statistical analysis and foundational machine learning techniques.
  • ● Genomics Foundation: Hands-on experience with NGS data analysis (e.g., RNA-seq, DNA methylation , ChIP -seq, or ATAC-seq).
  • ● Multi-omics Interest: Experience with, or a strong desire to learn, proteomic data analysis and multi- omic data integration.
  • ● Operational Skills: Excellent problem-solving skills, attention to detail, and the ability to manage multiple tasks in a fast-paced environment.
  • ● Communication: Ability to clearly present data and technical workflows to a multidisciplinary team.
  • ● Desired Skills and Attributes:
  • ● Prior experience or familiarity with biomarkers of immune system aging/function.
  • ● Prior experience or internship in the pharmaceutical or biotechnology industry.
  • ● Prior experience running large-scale association testing (e.g., genome-wide association studies GWAS , epigenome-wide association studies EWAS , proteome-wide association studies).
  • ● Familiarity with methods in statistical genetics (e.g., Mendelian randomization, fine mapping , colocalization).
  • ● Familiarity with machine learning analys is architectures (e.g., random forest, gradient boosting , transformers).
  • ● Familiarity with public biological databases (e.g., GTEx , TCGA ) , epidemiological cohort data (e.g., TOPMed cohorts) , or biobanks (e.g., UK Biobank, FinnGen ) .
  • ● Ability to apply integrated generative protein design pipelines - from target-conditioned backbone generation through sequence design to computational fold validation - to support the development of novel therapeutic biologics with optimized specificity and developability properties.
  • ● Working knowledge of computational histology pipelines incorporating modern deep learning approaches - including self-supervised and weakly supervised learning (MIL, DINO) and histopathology foundation models ( e.g. UNI, CONCH) - to enable scalable, label-efficient classification of complex tissue phenotypes.
  • ● Familiarity or prior experience with agentic AI in the context of analysis code pipeline development and biological analysis.
  • ● Evidence of scientific contribution through publications, posters, or GitHub repositories.
  • ● As AstraZeneca continues to put patients at the forefront of our mission, we are excited for our move to Kendall Square/Cambridge in 2026. Find out more information here: Kendall Square Press Release
  • ● Ready to join us on this mission Apply now