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 public biological databases (e.g., GTEx, TCGA),…