Computational Biologist

We are seeking a scientifically curious, technically strong, and self-driven **computational biologist** to join our team. In this role, you will work at the intersection of data science and biology - driving target discovery and biomarker identification efforts through independent exploration of proprietary and public datasets, statistical modeling, and high-throughput analyses. You’ll contribute directly to our discovery efforts, supporting early-stage research and helping shape hypotheses that matter for patient outcomes.

This position is ideal for someone who thrives in a **highly collaborative, mission-driven environment**, with a strong foundation in biology and biomedical research, who brings complementary strong computational skills, and enjoys making sense of complex biological data.

**WHAT YOU BRING TO THE TABLE**:

- Proficiency in a programming language for data analysis — **Python** preferred, but **R** and others are acceptable - along with experience in navigating large-scale datasets using **standard data science libraries** such as NumPy, pandas, polars, scikit-learn, matplotlib/seaborn, or equivalent tools for statistical analysis, modeling, and data visualization.
- Relevant post-graduate experience in drug discovery or applied biomedical research, ideally in a biotech or industry setting is strongly preferred.
- Experience working in **Linux environments**, and with **high-performance computing (HPC)**or **cloud platforms**.
- Comfort with **version control tools** like GitHub/GitLab, and collaborative coding practices

**WHAT YOUR MAIN TASKS WILL BE**:

- Analyze high-dimensional biological and biomedical datasets — including but not limited to transcriptomic, proteomic, imaging, and clinical data — to uncover and validate novel targets, biomarkers, or biological insights using statistical and machine learning approaches in close collaboration with interdisciplinary project teams.
- Independently identify, access, and integrate relevant public datasets (e.g., TCGA, GEO, Depmap) with proprietary data to support programs from target selection to early discovery.
- Develop and maintain reproducible pipelines for data processing and statistical analysis using best practices.
- Support research teams with robust, well-documented analysis workflows; contribute to the development of internal tools and infrastructure.
- Run large-scale analyses on high-performance computing clusters and cloud environments.
- Communicate findings clearly through visualizations, presentations, and collaborative discussions with wet-lab scientists and leadership.

**APPLICATION DETAILS**: