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Sanofi Group Statistical Modeling & Simulation CoE Team Lead in India

Job title: Statistical Modeling & Simulation CoE Team Lead

Location: IN / Hyderabad

About the job

Our Hubs are a crucial part of how we innovate, improving performance across every Sanofi department and providing a springboard for the amazing work we do. Build a career and you can be part of transforming our business while helping to change millions of lives. Ready? As Statistical Modeling & Simulation CoE Team Lead, you’ll be providing leadership, guidance, and strategic input as team manager. Direct statistical simulations support and tool development as necessary for realization of advanced innovative designs/novel methodology.

We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people’s lives. We’re also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?

Major Responsibilities

  • Define team members priorities, performance review and development areas. Responsible for appropriate resource planning and allocation. Promote teamwork, quality, operational efficiency, and innovation. Create productive work environment. Ensure team compliance with SOPs and departmental standards.

  • Responsible for directing the team to implement advanced statistical and/or AI/ML modelling approaches and conduct simulation studies to support trial design optimization and decision-making.

  • Ensure the execution of modeling and simulation tasks are aligned with the objectives, methodology and strategies defined by statisticians in Statistical Innovation Hub.

  • Verify the reliability and validity of deliverables through rigorous testing and validation processes.

  • Oversee data derivation and feature engineering with diverse data sources as necessary to prepare for subsequent modeling and simulation phases.

  • Keep up to date with the latest advancement in AI/ML technology, particularly in predictive modeling, causal inference, and complex data simulation.

About you

  • Experience: Experiences of statistical modeling and simulations and statistical tool development. Pharmaceutical experience is a plus.

  • Soft skills: Broad knowledge and experience of handling complex statistical or AI/ML models or large datasets, utilizing parallel computing, cloud computing platforms and version control systems.

  • Excellent R programming skills and good understanding of other computer languages and other statistical software.

  • Solid computation skills and good understanding of statistical concepts, particularly within the context of clinical development.

  • Experience in working with clinical trial data and/or other type of health records is a plus. Record of methodology research or demonstrated competency in innovative statistical designs through project applications is a plus

  • Technical skills: Demonstrated strong project/study management, interpersonal and communication skills.

  • Education: PhD/MS in statistics, computer science, mathematics or related discipline

  • Languages: Highly effective communication in English, both oral and written.

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 and at least 14 weeks’ gender-neutral parental leave.

  • Build, develop a team with expertise on modeling and simulations to accelerate clinical development and operations

  • Opportunity to apply most advanced AI/Machine Learning methods to increase the probability of success of clinical trials and improve operation efficiency

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