Scientist, Data Science Full-time NEW

Scientist Position in Early Oncology R&D

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 analyses 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.
• Team Participation: Actively participate in team meetings, presenting data-driven insights to help the group meet project milestones.
• Continuous Learning: Stay current with the latest developments in data science and bioinformatics tools.

Qualifications:

• Education: Ph.D. in Bioinformatics, Computational Biology, Data Science, Epidemiology, or a related field (0–2 years post-graduate experience); or MS with 2–4 years of experience; or BS with 4+ years of relevant experience.
• 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 analysis 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.

Competitive remuneration and benefits apply

We offer a competitive Total Reward program including a market driven base salary, bonus and long-term incentive. We have a generous paid time off program and a comprehensive benefits package.

The annual base pay for this position ranges from $91,008.80 – $136,513.20. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.