Head of In Silico Drug Toxicity, VP of Toxicology

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Deep Origin

Site WebLinkedInTous les Emplois

11 - 50 employés

🤖 Intelligence artificielle

🧬 Biotechnologie

🔬 Science

Artificial Intelligence • Biotechnology • Science

Deep Origin est une entreprise à la pointe de la technologie qui se concentre sur l'accélération de la recherche et du développement dans les sciences de la vie grâce à sa plateforme innovante et ses outils. Elle offre une boîte à outils de simulation moléculaire et une plateforme de R&D exploitant la puissance de l'intelligence artificielle et la gestion fluide des données. Les solutions de Deep Origin sont conçues pour aider les scientifiques à relever des défis complexes liés à la biologie et aux maladies en fournissant des outils pour l'analyse de données, la simulation moléculaire et la découverte de médicaments. L'entreprise vise à révolutionner la biologie computationnelle et à simuler des processus biologiques pour aider à découvrir de nouvelles thérapies et comprendre les maladies. Les offres de Deep Origin incluent des assistants d'analyse pilotés par l'IA, des stations de travail haute performance et des capacités de criblage virtuel de médicaments, en faisant un allié puissant pour la recherche et le développement scientifique interdisciplinaire.

Description

• Own, define, and scale our computational toxicology platform end-to-end. • Operate as the general manager of the platform with full ownership across scientific vision, technical architecture, product strategy, and execution. • Work cross-functionally with ML/AI teams, computational biologists, toxicologists, engineers, and commercial teams. • Translate cutting-edge science into scalable, enterprise-ready systems. • Define and drive the long-term roadmap for in silico toxicity prediction. • Design innovative modeling approaches across mechanistic and ML paradigms. • Integrate ML/AI, systems biology, PK/PD modeling, and real-world data into a unified platform. • Evaluate tradeoffs between mechanistic modeling and statistical learning approaches. • Identify breakthrough opportunities beyond current industry standards. • Translate scientific capabilities into robust, scalable software systems. • Partner closely with engineering to build secure, enterprise-grade infrastructure. • Ensure scientific rigor, reproducibility, and regulatory alignment. • Define product strategy for pharma-facing platform offerings. • Engage directly with senior R&D and safety leaders at pharmaceutical companies. • Build and lead a world-class interdisciplinary team (ML scientists, computational biologists, toxicologists, engineers).

🎯 Exigences

• 15+ years in computational biology, toxicology, drug discovery, or related domain. • Proven experience designing and deploying computational models for toxicity or biological systems. • Demonstrated ability to develop novel modeling approaches beyond industry-standard methods. • Deep hands-on experience designing and deploying computational toxicology models across mechanistic, statistical, and machine learning paradigms. • Demonstrated ability to design innovative modeling approaches, not just apply existing frameworks. • Expertise in integrating ML/AI, systems biology, PK/PD modeling, and real-world data into cohesive predictive systems. • Strong track record evaluating tradeoffs between mechanistic modeling and statistical/ML approaches. • Ability to identify breakthrough opportunities beyond current industry standards. • Experience developing novel model architectures, not just QSAR or legacy approaches. • Comfortable discussing mechanistic toxicity pathways, ML architectures, regulatory strategy, and platform design. • Deep expertise in one or more of: • - Predictive/computational toxicology. • - Systems pharmacology or systems biology. • - Mechanistic modeling. • - ML/AI in drug discovery. • - PK/PD or ADMET modeling. • Strong understanding of preclinical safety workflows. • Familiarity with regulatory frameworks (FDA, EMA, ICH). • Experience working with complex and proprietary pharma datasets. • Awareness of data limitations, bias, and validation challenges. • Experience leading large, interdisciplinary technical teams. • Ability to operate as both a strategic leader and a technical decision-maker. • Executive presence with senior pharma stakeholders. • Strong communication and cross-functional leadership skills. • Experience building platforms, teams, or systems from zero to scale. • Comfortable operating in ambiguity and defining direction. • Strong bias toward action, ownership, and iteration.

🏖️ Avantages

• Opportunity to define the future of drug safety and predictive toxicology. • Competitive compensation package with meaningful equity. • Comprehensive health, dental, and vision coverage. • Remote-friendly culture with optional onsite work. • Annual team gatherings and company events.