Head of Predictive Pharmaceutical Sciences, Pharmaceutical Sciences, R&D, Orion Pharma
About this role
Job Description
We are looking for a Head of Predictive Pharmaceutical Sciences to lead a specialist unit covering predictive modelling, data foundations and laboratory automation within Orion Pharma's Pharmaceutical Sciences department. The department is responsible for pharmaceutical development across Orion Pharma's therapeutic portfolio, with a particular focus on product development for synthetic modalities.
In this role, you will define and execute the strategy for predictive pharmaceutical sciences, strengthen the quality and usability of experimental data, and advance fit-for-purpose automation. By embedding mathematical, mechanistic, biopharmaceutical and forecasting models into development decision-making, you will help teams design robust medicinal products and manufacturing processes that support Orion Pharma's expanding clinical portfolio.
You will lead an expert team of data engineers and scientists in Finland and collaborate closely with laboratory units, the Pharmaceutical Sciences leadership team, and data specialists across Orion Pharma R&D. Through internal collaboration and selected external partnerships, you will play a pivotal role in translating technical innovation into better development decisions and meaningful impact for patients.
Responsibilities
- Build and lead a state-of-the-art predictive pharmaceutical sciences capability that combines mechanistic modelling, AI, high-quality experimental data and automation to improve drug product development.
- Coach a multidisciplinary team, be hands-on, lead by example and strengthen an environment of scientific curiosity, accountability and continuous learning.
- Combine first-principles and mechanistic modelling, statistical learning and modern AI methods, selecting the approach according to the scientific question, and available evidence.
- Establish rigorous prospective validation, uncertainty assessment and applicability criteria for predictive methods.
- Ensure that existing predict