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 a motivated and talented Sr. Scientist II/Associate Principal Computational Biologist to join the Computational Discovery Science team at Tempus. This scientist will contribute to a team focused on identifying novel targets for cancer therapeutics. This role will involve 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 requires creative thinking, a collaborative spirit, and a passion for groundbreaking research.
The ideal candidate will have a background in computational biology and translational medicine, with some experience in (or with) biopharma developing and applying statistical methods and machine learning to identify druggable targets in solid tumors. The candidate should be familiar with statistical methods used in clinical trials, real-world data analysis, and translational research in a pharma/biotech industry or academic setting. Additionally, familiarity with the use of patient-derived organoid models for target identification and validation, biomarker discovery, and identifying a drug’s mechanism of action (MOA) or preclinical proof-of-concept (POC) is a plus.
This position will involve contributing to data processing, analyses, and interpretation, and working closely with a team of scientists and engaging with science leaders at biopharma partners for strategic and scientific planning.
Key Responsibilities:
Execute Strategic Collaborations:
- Partner with our pharma clients to design, develop, and execute computational target discovery research projects leveraging the Tempus platform to advance precision medicine research programs.
- Develop and implement scientific strategies and experimental work plans, including identifying new methodologies and approaches for large clinico-genomic databases and PDOs.
- Integrate the above to prioritize target opportunities matched to biomarker and indication strategies that enhance the likelihood of developmental success.
- Evaluate clinical trial design by testing assumptions, refining eligibility criteria, and characterizing patient outcomes on standard of care.
- Develop novel biomarkers of response signatures.
Independent Contribution:
- Independently execute complex translational or real-world evidence research projects integrating molecular and clinical data from Tempus’ multimodal data platform to derive real-world insights for biopharma partners.
Scientific Communication:
- Expert in navigating client interactions; present scientific findings clearly and meaningfully to diverse sets of external stakeholders.
- Document, summarize, and communicate highly technical results and methods clearly to non-technical audiences.
- Author abstracts, posters, and peer-reviewed publications to illustrate the value of multimodal analysis and AI in drug discovery in coordination with our partners or internal R&D teams.
Continuous Improvement:
- Become an expert in our biopharma partners’ strategy, pipeline, and portfolio to proactively determine all areas that the Tempus platform could add value to the drug development process of our partners.
- Stay current with industry trends, best practices, and advancements in computational oncology research. Apply this knowledge to enhance research methodologies and improve overall research quality on the team.
Qualifications:
Education:
- PhD degree in a quantitative discipline (e.g., Biostatistics/Statistical Genetics, Bioinformatics, Computational Biology, Computational Immunology, or similar) plus 2 years of experience or postdoctoral studies. Alternatively, a PhD in Molecular Biology or another Life Science degree combined with a very strong record of computational biology.
Experience:
- Minimum 2+ years in drug development leveraging genomic and clinical/real-world data for drug discovery and development.
Technical/Scientific Skills:
- Proficient in R, Python, and SQL. Strong understanding of cancer biology.
Communication Skills:
- Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
Preferred Skillsets/Background:
- Strong understanding of statistical methods, molecular data, and machine learning in drug discovery with experience in integrative modeling of multi-modal clinical and omics data.
- Previous experience working with large transcriptome and NGS data sets.
- Prior consulting and/or client-facing experience is highly desirable.
- Ability to work collaboratively in a team environment.
- Thrive in a fast-paced environment and willing to shift priorities seamlessly.
- Experience with R package development.
- Strong peer-reviewed publication record.
- Experience with: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, RStudio, tidyverse, ggplot, Git, matplotlib, seaborn.
- Goal orientation, self-motivation, and drive to make a positive impact in healthcare.
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