Senior Scientist, Bioinformatics Engineer
About this opportunity
Location: Warsaw, PolandHybrid 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 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.
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.
How we do it
At AstraZeneca, we're dedicated to being a Great Place to Work, where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. There's no better place to make a difference in medicine, patients, and society. An inclusive culture that champions diversity and collaboration. Always committed to lifelong learning, growth, and development.
Date Posted
30-paź-2026
Closing Date
20-lis-2026
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 148 comparable Senior Biostatistics & Data Science roles across 31 biopharma companies.
Salary context
54 of 148 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 148 comparable roles
+ 13 more countries
Seniority mix
148 of 148 peers have a known seniority level
Therapeutic area mix
10 of 148 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.