Director, Biopharma Data Science

This is a fully remote position, open to applicants in France.

📋 Description

• Collaborate in the development of Owkin's AI Scientist focusing on both data-driven and knowledge-driven discovery methodologies.

• Lead and/or participate in initiatives aimed at identifying novel drug targets, biomarkers, and disease subtypes utilizing patients' multi-omics and pre-clinical datasets.

• Implement multimodal analyses and machine/deep learning techniques on various biological data types including genomics, transcriptomics, and proteomics.

• Work alongside biologists, medical doctors, computational chemists, agentic engineers, software developers, machine learning engineers, and external academic or pharmaceutical partners.

• Offer scientific insights to develop tools and functionalities required by pharmaceutical collaborators.

• Convey scientific findings to both internal and external stakeholders.

• Enhance Owkin's methodologies through continuous literature review.

• Build and maintain a high-performing, engaged team of scientists.

⛳️ Requirements

• PhD in Bioinformatics, Computational Biology, Data Science, Computer Science, or Machine Learning with a robust understanding of Computational Biology, or equivalent advanced industry experience.

• Extensive knowledge of multimodal datasets and omics modalities, including bulk RNA-seq, single-cell and spatial transcriptomics, WGS, WES, GWAS, and proteomics.

• Proficient in Python programming.

• Capable of producing clean, modular code that interfaces with complex APIs and databases.

• Established publication history in leading journals and conferences.

• Skilled at articulating complex agent behaviors to both AI and biological professionals.

• Exceptional written and verbal communication abilities.

• Proficiency in English; knowledge of French is advantageous.

• Competent in managing stakeholder expectations across various levels and cross-functional teams.

• Strongly collaborative with a team-oriented approach.

• Preferred: expertise in agentic AI, LLM reasoning, long-context management, and tool-augmented generation.

• Preferred: familiarity with medical and drug-discovery data types such as H&E and EHR.

• Preferred: experience in training and deploying large-scale models and orchestrating multi-agent systems.

• Preferred: background in causal inference and causal discovery within biological contexts.

• Preferred: proven experience in transforming research concepts into practical tools utilized by scientists.

🏝️ Benefits

• Flexible work arrangements.

• A welcoming and casual work atmosphere.

• Opportunity to collaborate with a diverse international team with high technical and scientific expertise.