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.

The ideal candidate has significant expertise in the genomics or clinical domain, and is eager to apply their skills to improve patient outcomes.

What You Will Do:

• Analyze large multimodal datasets to develop new AI-powered clinical reports
• Develop and characterize novel algorithms for predicting cancer subtype, patient outcome, and treatment response
• Collaborate with product, science, engineering, and business development teams to build the most advanced data platform in precision medicine
• Interrogate analytical results for robustness, generalization, and clinical impact
• Document, summarize, and present your findings to a group of peers and stakeholders

Required Qualifications:

• MS/PhD degree in a quantitative discipline (e.g. statistical genetics, cancer genetics, machine learning, bioinformatics, statistics, computational biology, biomedical informatics, or similar)
• Experience working with genomic (e.g., DNA-seq, RNA-seq) or clinical (survival data, trials, real world evidence, claims) data
• Outstanding data analysis skills, with a particular focus on detailed characterization of genomics and clinical datasets for powering machine learning algorithms
• Experience with supervised and unsupervised machine learning algorithms used in genomics and clinical research: regression, classification, survival modeling, clustering, dimensionality reduction, Kaplan-Meier, Cox regression
• Strong programming skills and experience with the python clinical+molecular data science stack: Pandas, NumPy, SciPy, Scikit-learn, lifelines, and Jupyter
• Strong database and SQL skills (BigQuery, dbt)
• Experience with engineering best practices for research computing (docker, git, code review, workflow managers, linux, cloud computing)
• Thrive in a fast-paced environment and able 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.
• Goal orientation, self motivation, and drive to make a positive impact in healthcare.

Preferred Qualifications:

• Strong peer-reviewed publication record
• 2+ years full time employment or postdoctoral experience building and validating predictive models on structured or unstructured data.
• Experience with traditional and deep learning approaches to survival modeling and subtyping
• Experience working with clinical cancer data (progression free vs overall survival, missing data etc.)
• Understanding of CLIA/CAP validation protocols and how to bring scientific ideas to market

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