Senior Scientist, Bioinformatics Engineer – AstraZeneca, Warsaw

Job Description

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 and QuBi Bioinformaticians supporting therapeutic areas across AstraZeneca, offering a unique opportunity to contribute to and apply new digital and AI capabilities. 
Based at Warsaw, Poland, 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 data from mass-spectrometer and emerging molecular assays. This is a hands-on technical role for someone who combines practical bioinformatics, workflow development, and data engineering skills with strong proteomics expertise to deliver robust, scalable workflows, data infrastructure, and tools that scientific teams can rely on.

Key Responsibilities

• Workflow development and automation
• Design, implement and maintain reproducible end-to-end pipelines for the processing, analysis, interpretation and visualisation of high-throughput proteomics data.
• Develop portable and versioned computational workflows using technologies such as Nextflow, Snakemake or comparable workflow frameworks.
• Containerise analytical environments using technologies such as Docker and deploy workflows across HPC and cloud computing environments.
• Build robust quality control, validation and provenance into computational workflows to produce reproducible, interpretation-ready outputs.
• Apply software engineering best practices including version control, code review, automated testing, CI/CD, documentation and release management.
• Design workflows and analytical components with appropriate interfaces, metadata and execution standards to enable programmatic reuse, including by emerging AI-enabled and agentic scientific workflows.
• Data management and infrastructure
• Develop and maintain scalable data models and storage strategies for proteomics raw data, intermediate results, processed datasets and analytical outputs.
• Establish metadata and provenance approaches that connect experimental context, sample information, analytical parameters, workflows and results.
• Integrate chemical biology, experimental and proteomics metadata to create harmonised, analysis-ready datasets that enable reuse and biological insight across studies.
• Apply FAIR data principles and appropriate data governance, access control and retention practices in collaboration with relevant technology and security teams.
• Contribute to searchable analytical data products and result databases using technologies such as Snowflake, PostgreSQL or equivalent platforms.
• Develop scientific interfaces and applications using technologies such as R Shiny, Streamlit or comparable frameworks where appropriate.
• Help make proteomics data and outputs discoverable, reusable and suitable for downstream advanced analytics, machine learning and AI applications

Qualifications

• Ph.D. (or Masters with 3 years of experience) in bioinformatics, computational biology, computer science, or a related discipline 
• 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.
• Experience developing reproducible computational workflows using Nextflow or Snakemake, together with containerised Linux environments.
• 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.
• Proficiency with version control (Git/GitHub) and collaborative software development practices.
• Ability to work as an independent contributor while collaborating effectively across multidisciplinary teams.
• Strong communication skills, with the ability to explain complex scientific and technical concepts to diverse audiences and support adoption of analytical solutions.

Skills Required

• Preferred qualifications:
• Experience in analyzing MS-based discovery proteomics data
• Experience developing or applying machine learning and AI for scientific data analysis, interpretation, or workflow automation.
• Experience in multi-omics analysis is a plus
• Solid grounding in the statistics of high-dimensional omics data
• A track record of scientific innovation demonstrated through publications, conference presentations, open-source contributions, software products, or deployed analytical tools in bioinformatics, AI, or data science.

Additional Information

Always verify vacancy details and availability on the employer’s official website. Jobs Bio Help Learning is an independent job-discovery platform and does not process applications.