Overview:
We are seeking a Lead Drug Discovery Scientist with a proven track record in advancing drugs through clinical phases who will be in charge of delivering the best repurposed drugs to our partners. The candidate will be a central element in our drug discovery strategy, bridging the needs in drug discovery with the development of our digital twin of the cell developed in R&D. This role requires a strong expertise in deep learning—preferably in foundation models—systems biology, and computational biology, especially in single-cell analysis.
Key Responsibilities:
• Repurposed Drug Delivery: Lead the identification, validation, and delivery of the best repurposed drugs to our partners, ensuring they are based on robust scientific evidence and align with partners’ needs.
• Drug Candidate Validation: Evaluate and validate drug candidates predicted by our computational models, ensuring they are of high quality and suitable for progression into clinical development and commercialization.
• Bridge R&D and Drug Discovery Needs: Act as a key liaison between the R&D team and drug discovery efforts, ensuring that the development of our digital twin of the cell aligns with practical drug development needs.
• Deep Learning Application: Utilize extensive experience in deep learning, preferably with foundation models, to enhance drug discovery processes and improve model predictions.
• Systems Biology and Single-Cell Analysis: Apply expertise in systems biology and single-cell analysis to interpret model outputs and understand the mechanisms of action of potential drug candidates.
• Client Engagement and Partnership Development: Collaborate closely with partners, presenting repurposed drugs and demonstrating the value of our models to pharmaceutical and biotech companies.
• Strategic Feedback to R&D: Provide informed feedback to the R&D team based on drug discovery needs and drug candidate development to guide the enhancement of our digital twin model.
• Leadership and Mentorship: Mentor junior scientists and contribute to team development and growth, fostering a culture of innovation and excellence.
• Interdisciplinary Collaboration: Work with biologists, chemists, clinicians, data scientists, and other stakeholders to translate computational predictions into actionable drug development strategies.
• Communication: Present complex scientific data and concepts clearly and persuasively to clients, partners, stakeholders, and team members, fostering strong relationships and facilitating successful collaborations.
• Continuous Learning and Innovation: Stay abreast of the latest developments in drug discovery, computational biology, deep learning, systems biology, and single-cell analysis to maintain cutting-edge expertise.
