Software Developer, Data Scientist, Data Analyst

Thank you for your interest in Stanford University. While we have instituted a hiring pause for non-critical staff positions, we are actively recruiting for most of the positions currently listed on our careers page. We will update the page when the broader hiring pause is lifted.

• DATE POSTED Sep 03, 2025
• Schedule Full-time
• Job Code 4821
• Employee Status Regular
• Grade I
• Requisition
• Work Arrangement On Site

The Department of Genetics in the Stanford University School of Medicine is a world-renowned hub for innovation in genomics, computational biology, and translational research. Our lab focuses on developing 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.

We are seeking up to two Software Developer 1 to join our interdisciplinary team and support the development of a modular automatic data analyses system for automating single-cell and spatial data analysis. The ideal candidate will contribute to the design, implementation, and scaling of cloud-based applications that power predictive models of organogenesis and disease progression.

This is an excellent opportunity to work at the forefront of biomedical data science, collaborating with world-class researchers, engineers, and entrepreneurs in a fast-paced academic and translational research environment.

• Assess user needs and requirements.
• 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.
• Design and implement user and operations training programs.
• Document changes in software for end users.
• Follow team software development methodology.
• Serve as technical resource with respect to applications.
• 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.

* - Other duties may also be assigned.

• Bachelor’s degree or higher in Computer Science, Bioinformatics, Biomedical Engineering, Management Science & Engineering, or a related field.
• Strong background in data science, or computational biology, especially in handling single-cell, spatial transcriptomics, or imaging data.
• Strong background in automatic data analyses tool development and deployment, distributed systems and others
• 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., Git Hub, Docker).
• Familiarity with scientific publication processes, manuscript preparation, and figures.
• Demonstrated ability to assess user needs, troubleshoot complex technical issues, and design user-friendly, scalable applications.
• Knowledge of research translation and commercialization strategy, including startup planning and engagement with venture capital firms.
• Experience preparing research presentations, grant proposals, or supporting industry/academic collaborations.
• Ability to work independently and collaboratively across interdisciplinary teams in a fast-paced academic or startup-like environment.
• Excellent communication and organizational skills, with attention to detail and strong documentation practices.

Bachelor's degree and three years of relevant experience or a combination of education and relevant experience.

• 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.
• Ability to recognize and recommend needed changes in user and/or operations procedures.
• Basic knowledge of software engineering principles.
• Strong knowledge of at least one programming language.