Computational Biologist, RNA at Tempus
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 to join our Computational RNA group. This individual will work in an interdisciplinary team to study transcriptome profiles in cancer using unique and growing collections of genomic data coupled with clinical data. The successful candidate will work in an interdisciplinary team, 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 research projects of high complexity.
- Evaluate new emerging technologies in healthcare.
- Develop the next generation of multi-modal products that will change clinical outcomes.
- Communicate highly technical results and methods clearly to non-technical audiences.
- Interact cross-functionally with a wide variety of people and teams.
- PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, bioinformatics, computational biology, or similar). Alternately, a PhD in molecular biology combined with a very strong record of high-throughput sequencing data analysis, or equivalent practical experience.
- Fluent with R, Python, or similar.
- Experience developing, training, and evaluating classical machine learning models.
- Previous experience working with large transcriptome data sets.
- Significant quantitative training in probability and statistics.
- Demonstrated willingness to both teach others and learn new techniques.
- Familiarity with common RNAseq databases such as TCGA, GTEx, and CCLE.
- Experience in network analysis.
- Statistical modeling experience
- Machine learning experience: low dimensional embedding, matrix factorization, supervised learning.
- Integrative modeling of multi-modal clinical and omics data.
- Survival analysis.
- Published peer-reviewed first author paper.