Tech Stack

Description

You will lead computational analysis of NGS data from large serine recombinase experiments, build integrated workflows combining proprietary and public datasets, and design scalable pipelines for multi-omics analysis. Collaborating closely with wet-lab teams, you'll transform experimental data into insights that drive next-generation genetic medicine development.

Requirements

• PhD with 5+ years or Master's with 10+ years industry experience in Computational Biology, Bioinformatics, or related field
• Strong expertise in experimental design, statistical analysis, and NGS data analysis
• Expert-level Python or R with data science libraries (Pandas, NumPy, Bioconductor)
• Experience with cloud computing and workflow management systems
• Excellent communication and cross-functional collaboration skills

Responsibilities

• Lead NGS data analysis from LSR experiments to quantify target efficiency and specificity
• Build integrated workflows to annotate and characterize target sites using proprietary and public genomic databases
• Design, implement, and scale robust computational pipelines for multi-omics analysis
• Collaborate with Screening, Genome Engineering, and Protein Engineering teams to design and interpret experiments
• Maintain internal databases and infrastructure for accessible, reproducible, and actionable results