About us
Owkin is an AI company on a mission to solve the complexity of biology. It is building the first Biology Super Intelligence (BASI) by combining powerful biological large language models, multimodal patient data, and agentic software. At the heart of this system is Owkin K, an AI copilot and its new LLM fine-tuned on biology called Owkin Zero, used by researchers, clinicians, and drug developers to better understand biology, validate scientific hypotheses, and deliver better diagnostics and therapies faster.
About the role:
We are seeking a detail-oriented and curious Bioinformatics Pipeline Developer to join our team. In this role, you will be at the intersection of biology and big data, helping to build and maintain the "highways" that process genomic, transcriptomic, clinical and other types of biological data. You won’t just be running scripts; you’ll be optimizing how we integrate diverse biological layers - from DNA sequencing to single-cell RNA-seq - to uncover clinical insights.
In particular, you will:
Build, test, benchmark, maintain and update automated workflows (AirFlow or Snakemake) for processing sequencing (WES/WGS, bulk and single-cell RNAseq, spatial transcriptomics) and other clinical, imaging and molecular data types.
Implement strategies to ensure data quality, consistency and interoperability.
Calculate, implement, benchmark and integrate biological information and scores based on biomedical scientist’s input.
Maintain clear, version-controlled codebases (GitHub) and technical documentation.
About you
Required Skills & Qualifications
A relevant education in bioinformatics, computational biology, computer science, or a related field (Bachelor, Master or PhD), with proven experience gained in industry or academia.
Proficiency in Python.
Experience of developing bioinformatic pipelines.
Experience working in cloud environments.
Familiarity with orchestration tools and frameworks
Experience with NGS tools such as variant calling and RNAseq analysis.
Basic understanding of biological processes such as NGS library preparation (e.g., Illumina, RNA-seq) and the biological differences between tissue types (e.g., enterocytes vs. colonocytes).
Comfortable using Git for collaborative development.
Nice-to-Haves
Proficiency in R.
Experience with Docker or Singularity containers.
Familiarity with single-cell/spatial transcriptomic analysis (Scanpy).
Knowledge of oncology-specific databases (TCGA, gnomAD, COSMIC).
What we offer
Flexible work organization
Friendly and informal working environment
Opportunity to work with an international team with high technical and scientific backgrounds