Description

You will develop systems and methods to support interactive analysis of large-scale genomic, cellular, and tissue profiling data.

Responsibilities

• Solve scientific software engineering challenges related to spatial transcriptomics data analysis and visualization.
• Build high-performance systems to query data from distributed sources for visualization interfaces.
• Collaborate with computational scientists, business analysts, and engineering teams to conceptualize software requirements.
• Contribute to the scientific community through open-source software development.
• Partner with local and offshore engineering teams to support development efforts.

Required Skills

• 5+ years of experience in Python, including designing high-performance systems and package development.
• 3+ years of experience developing Python tools for High Performing Computers and cloud solutions.
• Expertise with performant file formats such as HDF5, Zarr, or TileDB.
• Experience deploying software in AWS environments to establish APIs and data services.
• Proficiency with the scientific computing ecosystem, specifically NumPy and Pandas.
• Strong adherence to software engineering best practices, including version control, testing, and abstraction.
• Degree (BS or higher) in Bioinformatics, Computer Science, or a related field.

Preferred Skills

• Biological domain knowledge in single-cell genomics or data analysis of single-cell RNA-seq, CITe-Seq, or single-cell ATAC-seq.
• Familiarity with formal build, release, deploy, and continuous integration frameworks.

Key Skills

Education

Any Graduate