Stanford University's Department of Genetics is seeking to develop next-generation computational frameworks and automatic data analyses systems to analyze single-cell and spatial omics data, enabling new discoveries in developmental biology and disease modeling. The Software Developer 1 will support the development of a modular automatic data analyses system for automating single-cell and spatial data analysis and contribute to the design, implementation, and scaling of cloud-based applications that power predictive models of organogenesis and disease progression.
Requirements
- Proficiency in Python, R, or other scientific programming languages, with experience in software development and data pipeline automation.
- Experience with cloud computing platforms (e.g., AWS, GCP) and collaborative development tools (e.g., GitHub, Docker).
- Strong background in automatic data analyses tool development and deployment, distributed systems and others
- Working knowledge of latest software and design standards.
- Ability to define and solve logical problems for technical applications.
- Knowledge of and ability to select, adapt, and effectively use a variety of programming methods.
- Basic knowledge of software engineering principles.
Responsibilities
- Design and develop applications that may involve sophisticated data manipulation.
- Maintain and update existing programs.
- Troubleshoot and solve technical problems.
- Create programs to meet reporting and analysis needs.
- Document changes in software for end users.
- Follow team software development methodology.
- Serve as technical resource with respect to applications.
Other
- Assess user needs and requirements.
- Design and implement user and operations training programs.
- Support research translation, licensing, and commercialization strategy, including coordination with venture capital firms and Stanford’s innovation ecosystem.
- Assist with scientific writing, data visualization, and manuscript preparation.
- Ability to work independently and collaboratively across interdisciplinary teams in a fast-paced academic or startup-like environment.
