About this role
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. 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.
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).
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.
Impact: Your analyses turn raw data into the answe...