Sr Scientist II, Computational Discovery Science

Tempus AI•Redwood City, CA

1d•Remote

About The Position

Tempus is seeking a motivated and talented Senior Scientist II to join the Computational Discovery Science team. This scientist will contribute to a team focused on identifying novel targets for cancer therapeutics by developing and applying computational methods for drug discovery. You will leverage Tempus’ large clinico-genomic database alongside functional and molecular assays applied to patient-derived organoids (PDOs). This role demands a blend of creative and strategic thinking, leadership skills, and a passion for groundbreaking research. This is a highly collaborative role, requiring close partnership with AI/ML scientists, as well as computational and modeling lab biologists. You will also be client-facing, responsible for effectively communicating complex results to a diverse audience, including life science pharma and biotech partners.

Requirements

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.

Nice To Haves

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, NumPy, SciPy, 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.

Responsibilities

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.
In partnership with Tempus’ modeling lab, use 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 a cross-functional group of project and senior leadership stakeholders, both internal and external partners.
Present scientific findings clearly and meaningfully to diverse sets of external stakeholders and non-technical audiences.
Author abstracts, posters, and peer-reviewed publications to illustrate the value of multimodal analysis and AI in drug discovery.