Senior Scientist II, Computational Discovery Science

• Utilize novel analytical methods applied to multi-modal data—such as genomic, imaging, and clinical data to identify targets in patient sub-populations
• Apply in silico methodologies to Tempus Real-World Data (RWD) to identify molecular, biological, and clinical patterns associated with specific patient populations
• Leverage multimodal data to compress complex data into joint embeddings, enabling robust clustering of patients and the identification of novel molecular subtypes
• Utilize the LLM-orchestrated Tempus Loop Agent architecture to autonomously prioritize
• Partner with Tempus’ modeling lab, using data from CRISPR and cell perturbation experiments in patient-derived organoids to identify and validate novel targets
• Independently execute complex translational or real-world evidence research projects integrating molecular and clinical data from the Tempus multimodal data platform
• Work closely with cross-functional teams across R&D and the broader Tempus organization, including product engineering, clinical genomics labs, data science, and medical teams
• Communicate scientific and technical plans and outcomes to cross-functional project and senior leadership stakeholders, internal and external partners
• Present scientific findings clearly and meaningfully to diverse external stakeholders and non-technical audiences
• Author abstracts, posters, and peer-reviewed publications illustrating the value of multimodal analysis and AI in drug discovery

• PhD in a quantitative discipline (e.g., Bioinformatics, Computational Biology, Data Science) or Life Sciences with a strong computational publication record
• PhD with 4+ years of work experience leveraging genomic and multimodal data with machine learning approaches to address questions in complex diseases, especially cancer
• Proficient in R, Python, and SQL
• Strong understanding of Cancer, Genomics, and/or Immunology
• Extensive prior experience analyzing genomic data and running statistical/machine learning models
• Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences
• Multidisciplinary project team leadership experience and a demonstrated ability to lead complex projects
• Familiarity with the use of patient-derived organoid (PDO) models for target identification and validation, biomarker discovery, and identifying a drug’s mechanism of action (MOA) or preclinical proof-of-concept (POC)
• Experience with early-stage drug development, including target discovery and biomarker identification
• Experience analyzing single-cell RNA sequencing and spatial transcriptomics
• Proficient in computational biology packages and environments including Pandas, Num Py, Sci Py, Scikit-learn, Jupyter Notebooks, RStudio, tidyverse, ggplot, Git, Docker, and AWS
• Experience with R package development
• Comfort in a client-facing role with prior consulting and/or client-facing experience
• Ability to thrive in a fast-paced environment and willingness to shift priorities seamlessly

Demonstrates expertise in leveraging genomic and multimodal data through machine learning to identify targets in complex diseases, particularly cancer. Proficient in statistical analysis and communication of scientific findings to diverse audiences.

• PhD In Quantitative Discipline
• Proficient In R, Python, And SQL
• Experience Analyzing Genomic Data
• Multidisciplinary Project Team Leadership
• Experience With Early-Stage Drug Development

• Machine Learning Approaches
• Statistical Analysis
• Single-Cell RNA Sequencing
• Spatial Transcriptomics
• Computational Biology Packages
• R Package Development
• Data Analysis
• Target Discovery
• Biomarker Identification
• Clinical Data Integration

• Excellent Written And Verbal Communication
• Ability To Present Complex Information
• Client-Facing Experience
• Ability To Thrive In Fast-Paced Environment
• Leadership In Complex Projects

• Genomics
• Immunology
• Cancer
• Patient-Derived Organoid Models
• Real-World Evidence Research
• Drug Discovery
• Biomarker Discovery
• Mechanism Of Action
• Preclinical Proof-Of-Concept
• Complex Diseases

• Pandas
• Num Py
• Sci Py
• Scikit-Learn
• Jupyter Notebooks
• RStudio
• Tidyverse
• Ggplot
• Git
• Docker