Stanford University is seeking to integrate large-scale biomedical data sources into its clinical data warehouse and advance healthcare through AI, requiring the design, implementation, and maintenance of front-end and back-end solutions for healthcare applications that ensure data is FAIR.
Requirements
- Fluency in SQL, Python and R
- Strong knowledge of cloud platforms such as Google Cloud, Azure or AWS
- Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes
- Experience with CI/CD tools such as GitLab CI/CD or GitHub Actions
- Solid programming skills and experience in scripting
- Experience with data transformation and workflow tools such as dbt, Airflow or WDL
- Strong knowledge of database architecture best practices
Responsibilities
- design, implement, and maintain both front-end and back-end solutions for healthcare applications
- applying AI tools like Natural Language Processing (NLP) and Machine Learning (ML)
- managing data security
- troubleshooting complex technical problems
- Propose, conceptualize, design, implement, and develop solutions for difficult and complex applications.
- Contribute to all phases of a project, including systems analysis, program design, development, and implementation.
- Oversee testing, debugging, change control, and documentation for major projects.
Other
- Experience performing data analysis in research analyses and visualization work in a healthcare research or clinical setting
- Strong interpersonal and communication skills to interact with technical and non-technical stakeholders
- Excellent writing and analytical skills
- Five years of experience collaborating as a computational biologist or biostatistician
- Experience with working in a medical school environment, and working with HIPAA PHI and other clinical EHR databases
