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.
We are seeking an experienced scientist to lead Computational Discovery Science at Tempus. This scientist will be responsible for building a team and platform focused on identifying novel targets for cancer therapeutics. This role will be at the forefront of developing and applying computational methods for drug discovery using Tempus’ large clinico-genomic database, as well as functional and molecular assays - single cell, spatial, and functional genomics - applied to patient-derived organoids. This role demands a blend of creative and strategic thinking, leadership skills, and a passion for groundbreaking research.
The ideal candidate will be an expert in computational biology and high-dimensional genomic modeling, with a track-record in (or with) biopharma developing and applying systems biological approaches to identify druggable targets in solid tumors. The candidate must be experienced in designing functional genomic experiments, as well as utilizing existing public data sources, to infer cancer dependencies and prioritize therapeutic targets. Moreover, the candidate will have familiarity with the use of patient-derived organoid models, which may be used for target identification and validation, biomarker discovery, and identifying a drug’s mechanism of action (MOA) or preclinical proof-of-concept (POC).
This position is a hybrid senior analyst and leadership role. Contributions to data processing, analyses, and interpretation are expected, alongside overseeing a team of scientists and engaging with science leaders at biopharma partners for strategic and scientific planning.
Key Responsibilities:
SCIENTIFIC:
Utilize novel analytical methods applied to multi-modal data - such as genomic (bulk, single cell, spatial), imaging, and clinical - to identify targets in patient sub-populations
In partnership with Tempus’ modeling lab, use data from CRISPR and cell perturbation experiments in patient-derived organoids to identify and validate novel targets
Develop and implement scientific strategies and experimental work plans, including identifying new methodologies and approaches for large clinicogenomic databases and PDOs
Integrate the above to prioritize target opportunities matched to biomarker and indication strategies that enhance likelihood of developmental success.
MANAGERIAL:
Mentor and project manage a team of junior scientists or technicians to deliver on the project goals and objectives
COLLABORATION:
Communicate the scientific and technical plans and outcomes to a cross-functional group of project and senior leadership stakeholders, both internal and at biopharma partners
Work closely with other cross-functional teams across the R&D and broader Tempus organization (product engineering, operations, clinical genomics labs, medical, science, data science, etc) to integrate work plans and approaches
Foster a collaborative and inclusive work environment, promoting interchange of expertise between Tempus and biopharma partner contributors, and driving creativity, innovation, and scientific excellence.
Preferred Qualifications:
PhD with 6+ years of work experience
Education and experience must combine:
Quantitative and computational skills (e.g. Computational Biology, Biostatistics/Statistical Genetics, Bioinformatics, Biomedical Informatics, Biometrics, or Data Science for Health)
Biological or medical knowledge (e.g. Human Disease, Oncology, Genetics/Genomics, Molecular Biology, or Immunology)
Multidisciplinary project team leadership experience
Proficient in R or Python packages for computational biology
Ability to deliver actionable insights from NGS, clinical, and/or real-world data sets
Competency in statistical and mathematical techniques for biological data analysis
Experience with early-stage drug development, including target discovery and biomarker identification
Comfort in a client-facing role
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