Data Analyst, Tempus Modeling at Tempus
Tempus is modernizing pathological diagnosis and creating automated analytical systems for precision medicine. Our work helps patients find the most effective treatment available. We keep large data warehouses and strive to find new insights within that complex data.
To facilitate the investigation of novel insights we model disease and treatment with patient derived samples. The modeling lab generates varied data covering microscopic imaging, cell culture assays, CRISPR genetic screens, single-cell sequence analysis, and is continuing to push into novel molecular assay development. This work requires continual integration of data, automation of data collections, organization of data, and creation of analysis pipelines.
What You’ll Do:
- We are looking for someone who is quick learner, self-motivated, curious, gets to functional solutions quickly and continues to improve their projects over time and leaves a project organized, documented, automated and easy to repurpose. The ideal candidate will thrive in a work environment that demands creativity, teamwork, continual learning about new types of analytic approaches, data storage techniques, and best practices in computer science and data science.
- Maintain knowledge about new types of analytic approaches, data storage techniques, and best practices in computer science and data science.
- Work with physicians, geneticists, cancer biologists, and other computer scientists to improve data organization and our analytics.
- Oversee continual improvement and connectivity through multiple data sources generated by Modeling
- Generate data frameworks for various queries of Modeling operations and business development
- Computer science degree, or graduate work with a strong emphasis in data analysis
- Working knowledge of python, SQL, bash scripting, git/github, docker, and cloud services (aws or Google). Other languages like C, R, GO, julia are also appreciated though not required. Previous work in the domains of medicine or biological sciences is helpful, but not required. Industry experience in data science or software engineering is preferred.