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.

Key Responsibilities

• 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.

Qualifications

• 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.