Senior Scientist, Bioinformatics in Waltham, Massachusetts

Senior Scientist, Bioinformatics
- Proteomics

Make a meaningful impact on patients' lives around the world At AstraZeneca, we are combining cutting-edge science, data, and artificial intelligence to transform how we discover and develop medicines. As part of Data Sciences and Quantitative Biology (QuBi) this role will work in close collaboration with a group of chemical biology and proteomics scientists supporting therapeutic areas across AstraZeneca, offering a unique opportunity to contribute to and apply new digital and AI capabilities.

Based at our new Kendall Square site in Cambridge, Massachusetts, you will work at the intersection of mass spectrometry-based (MS) proteomics and computational biology, helping to build the data platform and analytical foundations that allow scientists to explore high-throughput proteomics and emerging molecular assays. This is a hands-on scientific and technical role for someone who combines strong proteomics expertise with practical bioinformatics, data science, and workflow development skills to deliver robust analyses, workflows, and tools that scientific teams can rely on.

What you will do As Senior Scientist, Bioinformatics
- Proteomics within QuBi, you will:

• Workflow development and automation
• Design, implement, and maintain end-to-end MS-based proteomics analysis pipelines (DDA and DIA; label-free, TMT/iTRAQ, SILAC) for the processing, analysis, interpretation, and visualization of high-throughput proteomics data.
• Wrap and orchestrate tools into portable, versioned workflows using Nextflow or Snakemake.
• Develop tools and workflows with the interfaces, metadata, and execution standards needed to support reuse by emerging agentic and AI-enabled systems.
• Containerize tool environments (Docker, Kubernetes) and deploy pipelines to HPC (Slurm) and/or cloud compute.
• Establish software engineering practices for the group - version control, code review, unit and regression testing, CI/CD, documentation, and release management.

• Data management and infrastructure
• Design and maintain a proteomics data model and storage strategy for raw files, intermediate results, processed matrices, and analysis provenance.
• Apply FAIR principles and appropriate data governance, access control, and retention policies in collaboration with IT/security.
• Build searchable result databases (using Snowflake/PostgreSQL) and interfaces (using R Shiny, or Streamlit).

• Analysis and scientific collaboration
• Perform proteomics analyses integrating chemical biology and experimental metadata to generate biological insight.
• Apply domain expertise in MS-based proteomics to support study design, data interpretation, quality assessment, and biological insight generation, particularly in support of chemical biology applications.
• Contribute to the development of scalable analytical methods and digital capabilities, including AI-enabled and agentic approaches, to support scientific discovery and decision-making.

Required qualifications

• PhD in bioinformatics, computational biology, proteomics, biostatistics, data science, computer science, or a related discipline, or equivalent research experience.
• Solid grounding in the statistics of high-dimensional omics data
• Demonstrated experience in bioinformatics and data analysis, with the ability to develop and apply reproducible analytical workflows for complex biological datasets.
• Experience with cloud platforms (such as AWS or Azure), covering core concepts like virtual machines, storage buckets, and basic cloud networking.
• Experienced in designing scalable omics data models and in applying FAIR data principles and data governance.

• Experience with programming and data analysis in languages such as R and/or Python, and familiarity with scientific software development best practices.
• Experience in building reproducible workflows with a pipeline framework (Nextflow, Snakemake) and containers in Linux environment.
• Proficiency with version control (Git/Git Hub) and collaborative software development practices.
• Ability to work as an independent scientific contributor while collaborating effectively across multidisciplinary teams.
• Strong communication skills, with the ability to…