Genomics Software Engineer for Spatial Omics

Position Overview
Background:
We are a group at gRED within the Scientific Insights Engineering department, with a focus on developing computational methods and tools to enable analysis of genomics data.
We have two overarching goals in the group: (a) drive forward Client science and discovery by building effective and efficient data analysis software systems; and (b) develop new cutting edge tools for genomic data analysis.

• We are looking for a genomics software engineer focused on imaging and spatial transcriptomics, to join our group to help with projects in the interactive analysis of large-scale cellular and tissue profiling initiatives using sequencing and imaging technologies.

Responsibilities:
Understand the problems the scientists need to solve, and work to find the best solutions to those problems. Work effectively with geographically diverse engineering teams and scientific collaborators, ensuring clear and timely communication among all parties.
Develop standardized image-based and spatial profiling data processing, from primary to tertiary analysis, and write up process documentation.
Ensure delivery of relevant tools and systems as workflows in WDL (Workflow Description Language), software packages in Python, and demos in Jupyter notebook.
Support data life cycle management projects that enable the storage, organization, dissemination and analytics of data in alignment with the scientific objectives of the scientific functional groups.

Requirements
Hands-on experience wrangling, analyzing and visualizing biomedical data. Although not a requirement, additional consideration is given to candidates with experience working with single-cell RNA-seq and/or spatial omics experiments (e.g. 10x Visium, nanoString CosMx, 10x Xenium, etc.).
Enthusiastic about working in a scientific environment, especially one focused on drug discovery and development.
Have a flexible learning mindset and be able to work in a fluid and dynamic environment.
Experience in software development using at least one of the mainstream programming languages (e.g. Python, R, C/C++, etc.). Although not a requirement, additional consideration is given to candidates with experience with Python.
Experience in workflow languages such as WDL and NextFlow is a plus, but not required.
UNIX/Linux operating system in the context of a scientific environment, especially one that is related to drug development.
Self-motivated and enthusiastic about programming and software tool development.
Demonstrated adherence to best practices in software engineering, particularly usability, version control, testing, and appropriate use of abstraction.
Demonstrated ability to effectively communicate about complex bioinformatics problems to peers, users and leadership.