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# Scientist, Data Science

- **Company:** AstraZeneca plc
- **Location:** Waltham, MA, United States
- **Experience:** Expert
- **Salary:** $118,870.0 - $178,306.0
- **Contract:** Internship / Graduate position
- **Skills:** Data Analysis, Bioinformatics, Clinical Data Repository, Computational Biology, Computer Programming, Databases, Data Integration, Data Visualization, Github, Python (Programming Language), Machine Learning, Random Forest, Xgboost
- **Published:** October 29, 2026
- **Apply:** https://astrazeneca.eightfold.ai/careers/job/563877691268110?domain=astrazeneca.com

## About the Role

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., * 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., * 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.
 * Evidence of scientific contribution through publications, posters, or GitHub repositories.

## Description

* 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.