Computational Science Lead

Reference No.R2844950

Position Title: Computational Science Lead

Department: Development AI

Location: Toronto, ON

Ready to push the limits of what’s possible? Join Sanofi in one of our corporate functions and you can play a vital part in the performance of our entire business while helping to make an impact on millions around the world.

At Sanofi, we chase the miracles of science to improve people’s lives. Within Digital R&D, the Integrative Clinical Data (ICD) team builds AI‑powered products that transform how clinical trials are designed, executed, and optimized.

This role sits at the intersection of trial design, operational analytics, and AI‑driven decision systems. You will lead the development of modeling and data frameworks that enable smarter trial design, real‑time operational insights, and scalable analytics across clinical programs.

You will work across end‑to‑end data flows – from raw clinical and operational data to production‑grade AI models and agentic systems. Your work will span in‑silico trial prediction, patient representation learning, disease progression modeling, clinical foundation models, with extensions into trial enrollment, site intelligence, probability of technical and regulatory success (PTRS) modeling, and end‑to‑end trial optimization with agents.

As a Lead Computational Scientist, you will operate as a technical owner across initiatives, driving modeling strategy, ensuring scientific rigor, and enabling deployment of decision‑grade insights into our Drug Development products.

We’re an R&D‑driven, AI‑powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

• Lead development of end‑to‑end clinical AI workflows, spanning data ingestion, curation, feature engineering, modeling, validation, and deployment across clinical trial design, execution, and optimization use cases
• Design, own and implement advanced modeling approaches for in‑silico trial prediction, patient representation learning, disease progression modeling and other development AI use cases – with an evaluation first mindset
• Translate clinical development questions into scalable computational solutions, partnering with clinical, biostatistics, and product teams to define appropriate modeling strategies and success criteria
• Drive integration of models into production systems and decision workflows, collaborating with engineering teams to ensure robustness, scalability, and usability
• Define and implement validation frameworks, including statistical evaluation, temporal validation, and alignment to clinical and regulatory expectations
• Communicate insights through clear narratives, visualizations, and decision frameworks, enabling adoption by clinical teams, study leads, and senior leadership
• Mentor and guide junior scientists, providing direction on modeling approaches, study design, and best practices in machine learning and data science
• Contribute to scientific leadership and external impact, including publications, conference submissions (e.g., ML4H, NeurIPS, AMIA), and cross‑industry/academia collaborations
• Identify and drive innovation opportunities across clinical AI, multimodal modeling, and agent‑based systems for trial operations
• Stay current with advancements in machine learning, generative AI, and clinical data science, and help translate these into practical applications across the organization

• 5+ years of experience in data science, machine learning, computational biology, or related quantitative fields, with demonstrated ownership of end‑to‑end analytical or modeling workflows
• Advanced degree (Master’s or PhD) in a quantitative discipline (e.g., computer science, statistics, engineering, computational biology, applied mathematics)
• Strong programming experience in Python (preferred), with deep familiarity in scientific computing and machine learning frameworks…