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 cancer profiles, extracting insights from large datasets. Responsibilities include designing and executing computational research projects, integrating diverse datasets, evaluating new technologies, and developing multi-modal products. The ideal candidate will possess a PhD in a quantitative discipline or equivalent experience, proven machine learning experience with genomics data, and proficiency in R, Python, and SQL. This role offers the opportunity to work on cutting-edge AI applications in healthcare and contribute to improving clinical outcomes. The position is remote and offers the opportunity to work in a fast-paced, collaborative environment.

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

#LI-Remote