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Salary: USD 145,000 - 190,000 / annual
Tempus is seeking a Senior Scientist II to join the Computational Discovery Science team, focused on identifying novel cancer therapeutic targets through computational methods and real-world evidence analysis. This role combines deep scientific research with strategic leadership and client-facing communication.
You will leverage Tempus' proprietary clinico-genomic database alongside functional and molecular assays applied to patient-derived organoids (PDOs) to drive drug discovery. Key responsibilities include:
**Scientific & Computational Strategy:** Develop and apply novel analytical methods to multi-modal data (genomic, imaging, clinical) to identify therapeutic targets in patient sub-populations. Use in silico methodologies on Tempus Real-World Data to uncover molecular, biological, and clinical patterns. Create joint embeddings from multimodal data to enable robust patient clustering and identification of novel molecular subtypes. Leverage the LLM-orchestrated Tempus Loop Agent architecture for autonomous target prioritization. Partner with the modeling lab to use CRISPR and cell perturbation data from patient-derived organoids to identify and validate novel targets.
**Leadership & Independent Contribution:** Execute complex translational and real-world evidence research projects independently, integrating molecular and clinical data from the multimodal platform. Collaborate across R&D, product engineering, clinical genomics labs, data science, and medical teams.
**Scientific Communication:** Present scientific and technical plans to cross-functional internal and external stakeholders. Communicate findings clearly to diverse audiences, including non-technical partners. Author abstracts, posters, and peer-reviewed publications demonstrating the value of multimodal analysis and AI in drug discovery.
This is a highly collaborative role requiring partnership with AI/ML scientists, computational biologists, and modeling lab teams, with significant client-facing responsibility.
**Requirements:**
- PhD in a quantitative discipline (Bioinformatics, Computational Biology, Data Science) or Life Sciences with strong computational publication record
- 4+ years of post-PhD work experience leveraging genomic and multimodal data with machine learning approaches to address complex disease questions, especially cancer
- Proficiency 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; ability to present complex information clearly to diverse audiences
- Demonstrated multidisciplinary project team leadership experience and ability to lead complex projects
**Preferred Qualifications:**
- Familiarity with patient-derived organoid (PDO) models for target identification, validation, biomarker discovery, and MOA/POC studies
- Early-stage drug development experience (target discovery, biomarker identification)
- Experience with single-cell RNA sequencing and spatial transcriptomics
- Proficiency in computational biology packages: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, RStudio, tidyverse, ggplot, Git, Docker, AWS
- R package development experience
- Client-facing or consulting experience
- Ability to thrive in fast-paced environments with shifting priorities
