Passionate about making a difference in the world of cancer genomics?
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 Systems Biology 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.
- Analyze and integrate large diverse clinical and molecular datasets to extract insights, and drive research opportunities.
- Evaluate new emerging technologies in healthcare.
- Develop the next generation of multi-modal products that will change clinical outcomes.
- Document, summarize and 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.
- Proficient in R, Python, and SQL.
- Experience developing, training, and evaluating classical machine learning models.
- Experience with integrative modeling of multi-modal clinical and omics data.
- Previous experience working with large transcriptome data sets.
- Thrive in a fast-paced environment and willing to shift priorities seamlessly.
- Experience with communicating insights and presenting concepts to diverse audiences.
- Team player mindset and ability to work in an interdisciplinary team.
- Strong peer-reviewed publication record.
- Strong knowledge of cancer or molecular and cell biology.
- Significant quantitative training in probability and statistics. Demonstrated willingness to both teach others and learn new techniques.
- Familiarity with common large transcriptome databases such as TCGA, GTEx, and CCLE.
- Experience in network analysis and survival analysis.
- Experience with supervised and unsupervised machine learning algorithms, and ensemble methods, such as: PCA, regression, deep neural networks, decision trees, gradient boosting, generalized linear models, mixed effect models, non-linear low dimensional embeddings and clustering.
- Experience in agile environments and comfort with quick iterations.