Senior Scientist, Bioinformatics Engineer

Location: Warsaw, Poland Hybrid model of work: 3 days in office, 2 remote per week 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. What  you will do As  Senior Scientist - Bioinformatics Engineer within QuBi , you will:  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   Required 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 networki