Computational Biologist/Senior Computational Biologist

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

We are seeking an independent and motivated Computational Biologist/Senior Computational Biologist to join our Computational Immunology group. This individual will work in an interdisciplinary team to study the tumor-immune microenvironment using unique and growing collections of genomic data coupled with clinical data. The successful candidate will carry out data analysis and apply best-in-class algorithms—or develop new algorithms— that directly address important biological and clinical questions.  

What You’ll Do

• Design, develop and execute computational immunology research projects of high complexity
• Work in interdisciplinary groups of scientists, engineers, and product developers to translate research into clinically actionable insights for our clients
• Collaborate on research projects with academic and industry research partners
• Produce high quality and detailed documentation for all projects

Required Qualifications:

• PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, bioinformatics, computational biology, or similar). Alternatively, a PhD in immunology, cancer biology or molecular biology combined with a very strong record of high-throughput sequencing data analysis
• Extensive prior experience analyzing genomic data, including whole exome sequencing, whole transcriptome sequencing, immune repertoire sequencing and especially single cell RNA sequencing
• Fluent with R or Python, and working knowledge of SQL
• Experience with communicating insights and presenting concepts to a diverse audience

Preferred Qualifications:

• Previous experience working with large clinical datasets (EHR, claims data, clinical trials, patient registries, etc), and/or experience with integrative methods to model multi-modal clinical and/or omics data
• Previous experience extracting and cleaning large datasets
• Expertise in immunology and cancer biology
• Experience with engineering practices for research computing (docker, git, code review, linux, cloud computing)
• Thrive in a fast-paced environment and willing to shift priorities seamlessly