Computational Systems Biologist

Summary

Join Tempus' Discovery AI group as a Computational Biologist to contribute to advancements in precision medicine. You will work on a team to analyze clinical and molecular data, develop AI algorithms, and extract insights to improve cancer treatment. This role requires a PhD in a quantitative discipline or equivalent experience, proficiency in various programming languages, and experience with machine learning models. You will design and execute research projects, evaluate new technologies, and communicate findings to diverse audiences. The position offers a competitive salary and a full range of benefits, including incentive compensation and stock units.

Requirements

• PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, bioinformatics, computational biology, or similar). Alternatively, a PhD in molecular biology combined with a very strong record of high-throughput sequencing data analysis, or equivalent practical experience
• Proven track record in executing machine learning models on genomics data
• 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

Responsibilities

• 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

Preferred Qualifications

• 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: tidyverse, ggplot, Git, matplotlib, seaborn, HTML5, CSS3, JavaScript, D3, Plot.ly, Flask, Dask, Docker, AWS
• 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

Benefits

• Incentive compensation
• Restricted stock units
• Medical and other benefits