Computational Immunologist - Omics Data, Analysis & Knowledge Mining (m/f/d)
Working at Alden Scientific Immune
Alden Scientific Immune runs the leading ImmunoINSIGHTS™ autoantibody biomarker discovery and immunological insights platform. In the last year our datagenerating lab scaled production 5x, and we are scaling 10x-100x over the next 24 months to meet demand.
As the measurement arm of Alden Scientific, we generate the high-quality immune data that feeds directly into Alden Scientific’s computational biology, AI modeling and clinical research platform.
Alden Scientific is a global physiology AI company transforming health and longevity by focusing on individuals, not averages. By combining multi-omic data with AI, it delivers predictive, personalized health management - making proactive health the new normal.
This is a unique opportunity to join a fast-growing and well-funded German biotechnology leader.
As the measurement arm of Alden Scientific, we generate the high-quality immune data that feeds directly into Alden Scientific’s computational biology, AI modeling and clinical research platform.
Alden Scientific is a global physiology AI company transforming health and longevity by focusing on individuals, not averages. By combining multi-omic data with AI, it delivers predictive, personalized health management - making proactive health the new normal.
This is a unique opportunity to join a fast-growing and well-funded German biotechnology leader.
About the Role
We’re looking for a computational data analyst with a strong background in immunology who turns large autoantibody and high-throughput multi-omic datasets into validated, biologically meaningful answers. You retrieve the data you need directly from our databases, develop and run analyses at scale, and decide whether a dataset is scientifically sound before it is used.
This role is based at our laboratory in Dortmund, Germany. Working next to the bench is part of the job: judging noise, latent process variables and data quality means knowing how the data was generated, and unexpected results are best checked with the people who produced them.
This role is based at our laboratory in Dortmund, Germany. Working next to the bench is part of the job: judging noise, latent process variables and data quality means knowing how the data was generated, and unexpected results are best checked with the people who produced them.
What You’ll Do - Data Quality & Statistical Validation
- Perform the scientific acceptance of datasets from our laboratory: content-level quality control, plausibility checks, biological validity and fitness for purpose.
- Distinguish genuine biological signals from technical and statistical artifacts (e.g., batch or plate effects, confounding study factors) and develop normalization procedures that correct for them.
- Critically assess results for robustness - effect sizes, multiple-testing correction, confounders and reproducibility across cohorts - before they are reported.
What You’ll Do - Analysis & Pattern Discovery
- Develop and run reproducible analysis workflows on large autoantibody and multi-omic datasets, applying biostatistics and machine learning to uncover patterns and generate new biological insights.
- Combine population-based analyses with longitudinal monitoring of individuals to distinguish normal variation from meaningful change in autoantibody profiles over time.
What You’ll Do - Scientific Output & Knowledge Building
- Interpret results biologically with our medical research team and translate findings into statistically valid, biologically plausible and understandable results.
- Build a knowledge pool of epidemiological autoantibody profiles from our own data, benchmark it against public reference sources, and annotate findings with public protein, epitope and disease databases (e.g., UniProt, IEDB, Open Targets).
What We’re Looking For
- PhD or equivalent research experience in bioinformatics, biostatistics, immunology or a related field.
- Strong background in and enthusiasm for immunology
- In-depth knowledge of autoantibody, proteomics and multi-omic data analysis, and excellent interpretation skills linking results back to the underlying science.
- Strong programming skills (e.g., Python, R, SQL or JavaScript) and the willingness to pick up new languages, coding environments and app deployment workflows.
- Readiness to work on site in Dortmund - fluent English and German.
Preferred Qualifications
- Experience with population-based or epidemiological studies.
- Experience running projects with pharmaceutical or biotech partners, contract research customers or grant-funded consortia, from study design to delivering results.
- Good overview of medical and biological online resources and databases, and how to use them.
Why Join Us?
- Impact: Your analyses turn raw data into the answers our customers, Alden Members and research partners rely on.
- Growth: Shape the analytical and interpretive core of our immunology research and grow with a young, expanding team.
- Innovation: Work with large, unique autoantibody and multi-omic datasets at the frontier of AI-informed health management.
- Collaboration: Work alongside lab scientists, immunologists and our US-based computational biology and AI teams.
- Benefits: Competitive salary and professional development support.
How to Apply
We value curiosity, scientific rigor, and a passion for turning data into a new understanding of human biology. We look forward to receiving your application, including your resume, a short cover letter and succinct answers to the questions below, by email to:
Gabriele Brauer, Alden Scientific Immune GmbH
Otto-Hahn-Str. 15, 44227 Dortmund, Germany
emailGabriele Brauer, Alden Scientific Immune GmbH
Otto-Hahn-Str. 15, 44227 Dortmund, Germany
- We build health around individuals, not averages. Why does that matter to you, and what should your work change for the people behind the data?
- Tell us about a dataset you decided was not fit for purpose. What made you distrust it, how did you establish that, and what happened next?
- Describe a pattern you found in a large dataset that nobody was looking for. How did you find it, and how did you establish it was real?
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