OWKIN
Senior Scientist – AI for Life Sciences
United Kingdom
Posted 13 days ago
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Scientist for AI in Life Sciences – Agentic AI & Drug Discovery
About the Role
We are seeking a hands-on scientist to accelerate our AI-driven drug discovery programs at Owkin. You will operate at the forefront of Agentic AI and Large Language Models (LLMs), applying these advanced technologies to address complex biological challenges in Oncology and Age-Related Diseases.
Key aspects of the role include:
- Ensuring biological relevance of AI models and tools by selecting datasets, defining features (biology-driven, AI-based, and expert-validated), and generating visualizations
- Designing in silico analysis plans for drug discovery tasks, including target identification and biomarker discovery
- Deploying and evaluating AI models while ensuring outputs align with biological and pharmacological criteria
Primary Location
Paris (office or remote from France, UK, or Germany)
Key Responsibilities
AI-Driven Discovery & Strategy
- Lead autonomous or semi-autonomous AI workflows (e.g., Owkin K) to generate, test, and refine hypotheses
- Participate in target identification (Target ID) and strategic analysis plans
- Conduct rigorous triage of target and asset candidates
- Transition high-volume AI outputs into actionable high-quality biological leads
- Optimise workflows and feedback loops for in silico drug discovery
- Perform deep-dive analyses on high-potential target/asset candidates to drive “go/no-go” decision milestones
- Collaborate with internal teams on subgroup identification to uncover novel patient populations and therapeutic niches
- Bridge computational and biological/chemistry teams in discussions on in silico drug design
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Biological Relevance of AI Models & Agents
- Design and refine functional “skills” for Agentic AI systems, including tool-calling functions and specialised reasoning pipelines (e.g., for Owkin K)
- Collaborate with data science and computational biology (compBio) teams to define and optimise scoring functions for target/asset prioritisation
- Ensure alignment between user feedback, product owners, and ground truth biological definitions
- Maintain ongoing evaluation of outputs across model iterations
Documentation & Cross-Functional Support
- Draft and maintain comprehensive project documentation
- Support data integration and mapping efforts, ensuring alignment with scientific objectives
- Partner with project managers and principal scientists to translate technical findings into clear deliverables
- Critically review AI-generated or CRO outputs for accuracy and relevance
- Assist in designing outsourced laboratory workflows for validation studies, scrutinising data quality against AI-driven hypotheses
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Requirements & Qualifications
Required
- Ph.D. in Biomedical Sciences or a closely related field
- Deep expertise in Oncology or Age-Related Diseases
- Multi-year industry experience, with a track record applying AI to Lifesciences/ex Drug Discovery
- Ability to translate AI potential into concrete drug-discovery milestones—especially in target identification & validation
- Proficiency in managing in-house AI workflows with minimal supervision
- Exceptional communication and cross-disciplinary project management skills (able to synthesise findings for non-specialist audiences)
- Familiarity with LLMs and Agentic AI frameworks (e.g., Prompt Design, Knowledge Integration)
- Full proficiency in English
- Curiosity-driven mindset: Comfortable deconstructing complex biological/AI systems and iterating rapidly
Preferred
- Demonstrated success with semi-automated tools/workflows for target prioritisation and AI-assisted biology
- Translational datasets and clinical/research experience, including safety assessments
- Hands-on experience with multi-omics technologiesphilis
- Prior CRO management for wet-lab validation studies
- Expertise in biologics, including Antibody (Ab) or Antibody-Drug Conjugate (ADC) research
Please submit your application in English.
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Skills
AI
Drug Discovery
Oncology
Age-Related Diseases
Target Discovery
Pipeline Management
Cross-Functional Collaboration
Large Language Models
Agentic AI
Computational Biology
Data Science
In Silico Drug Design
Documentation
Communication
Biologics
Multi-Omics
Location