Better is out there. Better medications, better outcomes, better science. But progress doesn't happen without people – people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let's be those people.
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The salary range for this position is :€113,760.00 – €151,680
Final compensation will be determined based on demonstrated experience, skills, location, and other relevant factors. Employees may be eligible to participate in Company employee benefit programs.
As Head of the Toxicology Innovation group in Germany, you will shape the future of computational toxicology at Sanofi — leading a multidisciplinary team of scientists to harness the power of AI, machine learning, and quantitative systems toxicology (QST) modeling to accelerate drug development and improve patient safety. Based in Frankfurt, Germany, you will be in Sanofi's Preclinical Safety department, driving digital transformation in close collaboration with in vitro innovation, and building the AI-ready infrastructure that connects toxicology to the broader R&D AI priorities and ecosystem. Ready to get started?
Sanofi's Preclinical Safety department is a global, science-driven organization committed to delivering innovative, data-informed safety assessments that support the development of life-changing medicines. The Global Toxicology Innovation group sits at the intersection of computational science, biology, and drug development — pioneering new approaches to toxicity prediction, mechanistic understanding, and clinical translatability.
Main responsibilities:
About You
To succeed in this role, you will bring a rare combination of deep scientific expertise, leadership experience, and a passion for digital innovation in drug development:
• Scientific expertise: Advanced degree (PhD preferred) in Toxicology, Computational biology/chemistry, Pharmacology, Cheminformatics, or a related discipline, with a strong track record in computational toxicology or quantitative systems toxicology modeling in a pharmaceutical or biotech setting
• Technical skills: Hands-on experience with AI/ML model development, mathematical (QST) modeling, and integration of computational tools into drug discovery and development workflows; familiarity with regulatory frameworks for in silico methods (e.g., ICH M7, OECD guidelines)
• Leadership & people management: Demonstrated ability to lead, inspire, and develop diverse, multidisciplinary scientific teams; experience defining team strategy, managing resources, and building individual development plans
• Cross-functional collaboration: Proven ability to work effectively across in vitro, in vivo, and project teams (discovery/clinical), as well as with digital and data science functions; strong stakeholder management skills
• Strategic mindset: Understanding of how preclinical safety data translates to human safety, ability to translate scientific vision into actionable roadmaps, connect digital toxicology to broader R&D strategy
• Communication & influence: Strong written and verbal communication skills in English (German is a plus); communicate complex concepts to diverse audiences at all organizational levels, ability to educate, advocate, and build consensus around digital innovation
• Languages: Fluency in English required; German is an advantage
Why Choose Us?
• Cutting-edge science: Work at the forefront of AI-powered drug development, shaping how toxicology is practiced globally
• Global impact: Lead a team with international reach, collaborating across Sanofi's worldwide R&D network
• Innovation culture: Be part of Sanofi's bold transformation into an AI-first biopharma company
• Collaborative environment: Partner with world-class scientists, digital experts, and business leaders across disciplines
• Career growth: Access to Sanofi's global learning ecosystem, leadership development programs, and SkillsPower upskilling platform
• Frankfurt hub: Join a vibrant scientific community in one of Europe's leading life sciences locations
Tagged as: Life Sciences
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