Computational Science Lead

Reference no. R2861855

Position Title: Computational Science Lead

Department: Development AI

Location: Toronto, ON

About the Job

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.

About Sanofi

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. 

Main Responsibilities:

• 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

About You

Qualifications:

• 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 (e.g., PyTorch, scikit-learn)
• Experience applying software engineering best practices to data and ML systems, including version control, testing, modular code design, and reproducible workflows
• Experience developing or applying agent-based or AI-driven decision systems , integrating machine learning models, data pipelines, and reasoning workflows to support complex tasks (e.g., clinical trial operations, monitoring, or optimization)
• Strong understanding of model validation, experimental design, and performance evaluation in real-world or clinical AI settings
• Experience working with data pipelines and large-scale datasets , including preprocessing, feature engineering, and reproducible workflows
• Ability to translate ambiguous business or clinical problems into structured analytical approaches
• Strong communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders

Preferred Qualifications:

• Preference for a track record of publications or contributions to machine learning conferences (e.g., NeurIPS, ICML, ICLR, ML4H) or related journals
• Preference for experience working with cloud platforms and data infrastructure (e.g., AWS, Snowflake, Spark/PySpark)
• Proven experience developing and deploying machine learning models on complex biomedical or clinical datasets (e.g., EHR, clinical trials, real-world data, imaging, multimodal data)

Why choose us?

• Bring the miracles of science to life alongside a supportive, future-focused team. 
• Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally. 
• Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact. 
• Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high-quality healthcare, prevention and wellness programs.

This position is for a new vacant role that is now open for applications.​

AI Usage

"Artificial Intelligence” refers to any systems that use automated processes, including algorithms and machine learning, to analyze data and make predictions, inferences, decisions, or recommendations without direct human involvement. These systems may process personal information to identify patterns, improve services, or support decision-making. The Company may use Artificial Intelligence for purposes including, but not limited to, resume screening and hiring, scheduling interviews or meetings, conducting surveys, matching skills with potential job openings, interview scoring, ensuring compliance with regulations applicable to our industry, and activities related to performance evaluation. Information collected and processed by the Company’s Artificial Intelligence systems may include the personal information detailed above and calendar availability. It excludes the information collected and processed for monitoring purposes. You should contact Human Resources if you have a question or concern regarding your personal information. You can also contact Canada’s Privacy Officer via Sanofi’s data subject request portal,   Data Subject Rights Webform . The Data Subject Rights Webform can also be used to request access or correction of your personal information and file a complaint.

Sanofi is an equal opportunity employer committed to diversity and inclusion. Our goal is to attract, develop and retain highly talented employees from diverse backgrounds, allowing us to benefit from a wide variety of experiences and perspectives. We welcome and encourage applications from all qualified applicants.  Accommodations for persons with disabilities required during the recruitment process are available upon request.

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All compensation will be determined commensurate with demonstrated experience. Employees may be eligible to participate in Company employee benefit programs, and additional benefits information can be found here.

Toute compensation sera déterminée en fonction de l'expérience démontrée. Les employés peuvent être admissibles à participer aux programmes d'avantages sociaux de l'entreprise. Des informations supplémentaires sur les avantages sociaux peuvent être trouvées ici.