Senior Scientist, Data Science

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