Computational Scientist

Responsibilities

• Analyze large-scale, high-content sequencing-based perturbation datasets to generate actionable biological insights.
• Develop and apply computational methods for analyzing multi-conditional perturbation datasets, including technologies such as Sci-Plex, Perturb-seq, and CROP-seq.
• Collaborate closely with cross-functional teams including biologists, chemists, data scientists, and other research stakeholders to support therapeutic discovery programs.
• Perform statistical modeling, probabilistic analyses, and advanced data interpretation to support research objectives.
• Develop, maintain, and optimize scientific workflows using Python and computational pipelines.
• Present findings, prepare scientific summaries, and communicate results to multidisciplinary research teams.

Qualifications

• PhD in a quantitative discipline such as Computational Biology, Bioinformatics, Computer Science, Statistics, Mathematics, or in the physical/life sciences (Chemistry, Biology) with a strong quantitative focus.
• Demonstrated experience analyzing large-scale multi-conditional single-cell RNA sequencing (scRNA-seq) datasets.
• Strong scientific programming skills in Python.
• Solid foundation in statistics, probabilistic modeling, and computational data analysis.

Preferred Qualifications

• Experience analyzing Perturb-seq, CROP-seq, CRISPR screening, or related perturbation datasets.
• Experience integrating multimodal datasets, including clinical and patient-derived data.
• Familiarity with workflow management tools such as Nextflow or Snakemake.
• Hands-on experience working in High-Performance Computing (HPC) environments and using SLURM.