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Global Head of RnD Data and Computational Science

Sanofi
Full-time
Remote friendly (Morristown, NJ)
United States
$240,000 - $346,666.66 USD yearly
Corporate Functions

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Role Summary

Global Head of RnD Data and Computational Science leads Sanofi's enterprise-wide data science and computational capabilities, driving innovation at the intersection of digital technology and R&D. This role sits within the Digital organization and coordinates cross-functional collaboration between enterprise Data & AI teams, R&D business functions, and software engineering to accelerate scientific breakthroughs and optimize R&D outcomes.

Responsibilities

  • Enable scientific breakthroughs and business transformation through computational methods, in-silico workflows, and operational excellence that empower R&D teams to optimize experimental designs, focus capital allocation, and accelerate the pathway of new molecules to patient impact.
  • Translate R&D digital strategies and scientific objectives into a coherent computational science strategy with roadmaps across data science, AI technologies, and in-silico workflows enabling scientific discovery and data-driven R&D decision making.
  • Lead a diverse global community of computational scientists, data scientists, and AI experts; manage performance, talent development, and capability building to keep pace with evolving technologies.
  • Engage externally as a thought leader for Sanofi's computational science and AI-driven R&D initiatives; manage publications and patents adhering to policies.
  • Directly manage R&D's Guiding Coalition for Computational Science and AI Innovation; prioritize investments and monitor progress to sustain value delivery and impact.
  • Plan delivery through in-house expertise, academic partnerships, and industry collaborations; maintain up-to-date information on plans, progress, and ROI.
  • Collaborate with legal, quality, and compliance to monitor regulatory landscape and balance innovation with risk mitigation in AI/ML applications.
  • Monitor advancements in computational science; advise on build/buy/partner decisions and participate in strategic partnering as needed.
  • Oversee quality delivery by academic partners, technology vendors, and consultancies in computational science and AI within R&D.
  • Foster alignment between computational approaches and enterprise data architecture, promoting integration of wet lab and in-silico workflows.

Qualifications

  • PhD in Computational Biology, Bioinformatics, Data Science, or a related field required.
  • 12+ years in pharmaceutical/vaccine/biotech R&D with deep understanding of scientific workflows.
  • 8+ years leading computational science, data science, or AI initiatives within large, complex R&D organizations.
  • Expertise in advanced computational approaches (simulation, modeling, machine learning, deep learning) and their application to drug discovery and development.
  • Business acumen and a track record of delivering computational solutions with measurable impact (accelerated timelines, improved success rates, optimized resources).
  • Experience integrating computational approaches with experimental workflows and translating in-silico insights into actionable R&D decisions.
  • Experience partnering with academic institutions, technology providers, and computational science innovators.
  • Experience leading multidisciplinary teams in a matrixed, global organization.
  • Ability to be present in Sanofi offices 2-3 days per week; up to 30% travel to HQs and major R&D centers globally.
  • Native English; business French is a plus.
  • Strong communication, collaboration, and accountability with a results-oriented mindset.

Skills

  • Advanced computational methods (simulation, modeling, ML/DL) for drug discovery
  • Strategic planning and program management for large data/AI initiatives
  • Leadership of global, cross-functional teams
  • Partnership development with academia, industry, and technology providers
  • Regulatory and compliance awareness in AI/ML within R&D

Education

  • PhD in a relevant field (Computational Biology, Bioinformatics, Data Science, or related)
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