Comprising diverse researchers from a variety of disciplines across academic divisions, the UC Santa Cruz Genomics Institute leads UC Santa Cruz's efforts to unlock the world's genomic data and accelerate breakthroughs in health and evolutionary biology. Our platforms, technologies, and scientists unite global communities to create and deploy data-driven, life-saving treatments and innovative environmental and conservation efforts, while addressing the most fundamental societal and policy questions.
The Computational Genomics Platform (CGP) team uses big data technologies to facilitate genomic research for global health issues such as breast cancer and other genetic risk factors. The team collaborates with many universities and institutes around the world. Examples of projects include supporting a global team to create the Human Cell Atlas, a compendium that maps gene activity in each cell in the human body, and the National Health Institute Data Commons project, which is about developing the technology to share and combine controlled access to genomic data across different institutes. If you are excited to work in a fast-paced, startup like environment within an institution of higher education in beautiful Santa Cruz, California, we encourage you to apply.
The Director of the Computational Genomics Platform (CGP) leads a team of technical and administrative professionals to develop cloud-based solutions for genomics data processing. Through their own technical leadership and the management of subordinate supervisors and staff, they create scalable cloud-based storage systems, analysis frameworks, and visualization tools used for analyzing genomics and biomedical data, including making it accessible for machine learning. The incumbent fosters collaborative relationships with internal and external entities to further the goals of CGP and the Genomics Institute and works with research groups at other institutions to integrate infrastructure across systems using GA4GH standards. The Director ensures that CGP's financial and human resources are managed effectively and efficiently in accordance with university policies and procedures as well as local, state, and federal regulations and law.
Qualifications / Competencies
- Bachelor's degree in biological science, computational / programming, or related area and / or equivalent experience/training.
- Experience and demonstrated skill managing a technical staff of IT and / or scientific personnel, including projects performed by multiple developers.
- Experience with iterative and test-driven software development.
- Broad knowledge and experience in bioinformatics or software programming design, modification and implementation, methods, data security, operating systems, relational databases, and data structures in an open source environment.
- Experience working in UNIX or Linux environments with large cloud deployments, large databases, and large code bases.
- Experience in Java and Python programming languages.
- Demonstrated interpersonal skills and ability to work cooperatively and effectively with others at all levels of the organization and with outside collaborators.
- Advanced analytical ability to quickly grasp new concepts, integrate them into projects as appropriate, and to rapidly and effectively determine the required action in a new situation.
- Demonstrated project management, business process optimization and operations management experience in a biomedical, research, or software development organization.
- Ability to communicate technical and non-technical information and scientific concepts in a clear and concise manner in English.
Preferred Qualifications / Competencies
- Advanced degree in related area preferred.
- Broad knowledge of modern biology or applicable field of research.
- Experience developing, deploying and managing web portals.
- Experience with Docker.
- Experience on the AWS, Azure, or Google cloud environments including VM, storage, and network services.
- Experience on source control, continuous integration, and tool redistribution systems.
- Experience in machine learning, including current high-level frameworks.