Tech Stack

Description

You will implement computational and statistical methods for single-cell and spatial transcriptomics data, derive clinical insights from large-scale datasets, and design biomarkers for diagnostic applications. This role involves building reproducible bioinformatics pipelines and analyzing thousands of samples to power next-generation clinical diagnostics.

Requirements

• Ph.D. in bioinformatics, computational biology, genomics or a related discipline with extensive hands-on experience in single-cell NGS data analysis.
• A minimum of 2 years of industry experience post Ph.D.
• Experience analyzing large-scale single-cell or spatial transcriptomics datasets to derive biologically meaningful insights and/or diagnostic biomarkers.
• In-depth understanding of the assumptions, limitations and caveats of statistical methods.
• Experience developing and optimizing high-performance, scalable code.
• Proficiency working in a Linux environment.

Responsibilities

• Implement rigorous computational/statistical methods for single-cell and spatial transcriptomics data analysis.
• Derive actionable insights from clinical/translational single-cell or in-situ spatial datasets.
• Design, implement and validate biomarkers for diagnostic applications.
• Implement and maintain bioinformatics pipelines for reproducible, large-scale data processing.
• Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples.