# Senior Scientist, Data Science at AstraZeneca

AstraZeneca is hiring a Senior Scientist, Data Science. It pays $119k-$178k a year and Level rates it Little AI ●○○○; you can [apply on AstraZeneca](https://jobsbylevel.com/go/03486c2e-a454-44c9-a0c7-eab170570df1?utm_medium=ai-agent).

AI Level 1, AI centrality 11 out of 100. US - Waltham - MA.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 1 (score 11 out of 100)
- Location: US - Waltham - MA
- Salary: $119k-$178k
- Posted: October 28, 2026
- Apply: https://jobsbylevel.com/go/03486c2e-a454-44c9-a0c7-eab170570df1?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-scientist-data-science-at-astrazeneca-7021b6

## Description

We are seeking a highly motivated Senior 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). Data Experience: Minimum 2 years of experience working with large patient-derived ‘omics datasets and clinical data to identify disease-associated pathways, candidate therapeutic targets, and biomarkers. Proteomics Experience : Minimum 2 years of experience developing reproducible pipelines for proteomics preprocessing, QC, harmonization, and performing proteome-wide association studies. Perturbation Analysis Experience : Working experience designing and analyzing CRISPRi perturbation studies, integrating splicing, transcriptomic, and proteomic evidence to resolve molecular mechanisms. 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). Familiarity or prior experience with agentic AI in the context of analysis code pipeline development and biological analysis.

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-scientist-data-science-at-astrazeneca-7021b6

Source: https://jobsbylevel.com/jobs/senior-scientist-data-science-at-astrazeneca-7021b6

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