COMPANY DECK
WHAT ARE WE LOOKING FOR
We’re building a new kind of drug company, one where aging is treatable, resilience is restorable, and the limits of lifespan are pushed by design.
Computational Biologist / Bioinformatics Scientist
We are seeking a talented Computational Biologist or Bioinformatics Scientist to advance our target discovery platform. In this role, you'll work with cutting-edge multi-omics data to identify promising therapeutic targets and accelerate drug discovery.
You'll be responsible for processing large-scale datasets from genetics, single-cell, and proteomics studies. Your work will involve building reliable analysis pipelines, implementing SOTA methods, and developing production-ready code. The ideal candidate combines strong computational skills with a passion for translating complex biological data into actionable insights for drug development.
To apply please send your CV (indicate the title of the job post in the subject of an e-mail).
Send CV- Data management & preprocessing: tabular data, genetics data (plink format), GWAS sumstats, xQTL, RNA-Seq (bulk, single-cell), proteomics.
- Association Analysis: WGS and WES common and rare variants associations, statistical inference, classical ML.
- Post-GWAS integration & validation: genetic colocalization, mendelian randomization, transcriptomics and proteomics integration.
- Pipeline development & maintenance: design, build, and maintain automated analysis pipelines for association studies, ensure code quality.
- Education: BSc/MSc/PhD in Computational Biology, Bioinformatics, Biostatistics, Computer Science, Genetics, or related quantitative field.
- Minimum of 3+ years (BSc) or 1+ years (MSc) of relevant experience (or relevant doctoral research for PhD).
- Demonstrated hands-on experience analyzing large-scale omics datasets (GWAS, RNA-seq, proteomics, or other omics data).
- Advances Proficiency in Python: pandas/polars/spark, matplotlib+seaborn, scipy, scikit-learn, statsmodels.
- Intermediate R proficiency: ability to implement methods from research papers and modify existing code.
- Proficient in a Unix/Linux command-line environment.
- Practical experience with genetics and GWAS data preprocessing and analysis (plink, normalization, harmonization).
- Experience with implementing post-GWAS methods (e.g. finemapping, meta-analysis, etc.).
- Experience with workflow management systems (e.g., Nextflow, Snakemake, CWL) is highly desirable.
- Familiarity with bulk RNA-Seq and/or scRNA-Seq principles.
- Strong problem-solving skills and analytical thinking to address complex biological questions.
- Ability to work both independently and collaboratively in a fast-paced research environment.
- Proactive approach to learning new technologies and methodologies as the field evolves.
There are no open positions currently
If you think you could be a good fit for the team, please email the Careers team using the link below.
Send CV
