In Silico Translational Disease Modeling Product Line Owner

Job Title: In Silico Translational Disease Modeling Product Line Owner

Location: Cambridge, MA, Morristown, NJ, Swiftwater, PA

About the Job

At Sanofi, we’re committed to providing the next-gen healthcare that patients and customers need. It’s about harnessing data insights and leveraging AI responsibly to search deeper and solve sooner than ever before. 

The Digital In-Silico Research team is a key innovation engine within Digital R&D, dedicated to pioneering next-generation digital products that reshape how R&D discovers, designs, and develops new medicines. We harness cutting-edge AI, machine learning, and computational modeling to build transformative in-silico solutions—empowering scientists with predictive insights, streamlining complex workflows, and turning data into decisive action. Our team plays a critical role in accelerating and transform drug discovery and translational research by building AI-powered, in-silico solutions that empower scientists, enhance decision-making, and connect data to action.

As the In Silico Translational Disease Modeling Product Line Owner, you will lead the development and delivery of sophisticated modeling platforms that transform how translational scientists understand disease mechanisms, predict drug responses, and identify and analyze biomarkers. Collaborating closely with quantitative pharmacology teams, toxicologists, and translational scientists, you will define the vision, strategy, and roadmap for computational tools that enable mechanistic disease modeling, safety prediction, and biomarker development. Your leadership will ensure these platforms effectively integrate diverse data types to generate actionable insights that accelerate drug development and improve clinical success rates.

You will manage and oversee Product Owners within your specific product line throughout the delivery of the products from ideation and execution through run and scale, overseeing the development and lifecycle management of your portfolio of products, ensuring alignment with R&D business goals and user needs. Collaboration across the organization will be key to delivering a portfolio of high-quality products that drive value for Sanofi.

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?

 Main Responsibilities:

Demand Intake & Management:

• Collaborate closely with translational medicine teams to understand their modeling and simulation needs across QSP (Quantitative Systems Pharmacology), toxicology, biomarker analysis or other teams involved in modelling systems to inform clinical development.

• Lead strategic demand ideation for translational modeling platforms, coordinating with disease area experts and quantitative scientists

• Work with team leadership to develop long-term strategy for advancing computational approaches to understand diseases

Vertical Product Delivery:

• Oversee development of integrated modeling platforms that support in depth disease understanding and biomarker identification

• Manage execution of modeling solutions that enable prediction of drug efficacy, safety, and optimal dosing

• Drive product development through required governance with focus on scientific validation and clinical impact

• Ensure proper integration of modeling tools into translational research workflows

• Collaborate with data science teams to implement advanced analytics for complex biological systems; build a culture that fosters knowledge-sharing amongst teams, aiming to resolve any blockers and accelerate product delivery

• Be the voice of the customer to drive the development of AI-powered tools for disease and drug modeling, toxicology and safety prediction, and drug response

• Work with data science teams to ensure proper validation and benchmarking of AI models against experimental data