Translational Scientist (Senior)
On-siteParis, Île-de-France, France
Job Summary
Dive into clinical, histopathological, and spatial transcriptomics data to investigate tumor biology and discover novel biomarkers predictive of treatment response. Translate mechanistic insights into target and biomarker hypotheses that bridge traditional biology, clinical practice, and state-of-the-art foundation models to improve real-world healthcare and clinical trial success. Collaborate with the R&D team to guide model development, interpretability, and feature design while working closely with the Clinical Data Manager to ensure data linkage. Stay up-to-date with latest discoveries in oncology and immunology modalities.
Required Qualifications
- Advanced clinical/ science degree (PhD, PharmD, MD)
- Ph.D. in cancer biology, molecular biology, genomics, or a related field
- MD with a strong background in oncology and exposure to computational concepts
- Strong understanding of cancer biology, tumor evolution, immune microenvironment, therapeutic mechanisms, response/resistance biology, and how these connect to patient outcomes
- Solid understanding of clinical trials and oncology drug development to bridge it to cellular and molecular immunology
- Experience with analyzing biological samples to identify disease indicators and guide targeted, personalized treatments
- Excellent communication skills
- Willingness to work in a collaborative environment with a multifunctional team spanning AI researchers and computational biologists
- Deep expertise in preferably colorectal cancer or lung cancer with previous projects targeted around either of the two
- 1+ year in a clinical/ medical/ pharmaceutical or biotechnology environment
- Track record of lead-author publications in cancer biology or oncology at high-impact journals (Nature, Science, Cell, Nucleic Acids Research, Genome Biology, ...)
- Hands-on experience with at least one of the following modalities: H&E, bulk-RNAseq, spatial transcriptomics, spatial proteomics, single-cell RNA-seq or high-content/pooled perturbation screens
- Ability to leverage Python and reproducible computational workflows to query and analyze complex, longitudinal clinical datasets
- Experience or prior knowledge on small molecules, nucleic acids, peptides, enzymes, antibodies, cell therapies and stem cell treatments
- Familiarity with clinical trial governance and healthcare policies required to bring interventions to market
Desired Qualifications
- 'team-first' attitude
- Independent work style
- Curiosity
- Exceptional attention to detail
- Ability to thrive in a dynamic, fast-paced environment
- Fun to work with
- Deep domain expertise, coupled with a passion for demonstrating the tangible impact of AI on real biology
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