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Merck is seeking an Associate Principal Scientist to join our Digital Insights team within DSCS Digital Technologies. The role focuses on deploying mechanistic, CFD-based, and data-driven modeling to design, de-risk, and optimize sterile DS/DP manufacturing processes.
The successful candidate will sit at the intersection of physics, computational fluid dynamics, and machine learning, driving a digital-first approach across development sciences and clinical supply to enable faster experiments and
We are a global biopharmaceutical leader with a different portfolio of prescription medicines, oncology, vaccines and animal health products. We are driven by our purpose to develop and deliver innovative products that save and improve lives. With 69,000 employees operating in more than 140 countries, we offer state-of-the-art laboratories, plants and offices that are designed to inspire our employees as we learn, develop and grow in our careers. We are proud of our over 125 years of service to humanity and continue to be one of the world's biggest investors in Research & Development.
We are seeking an Associate Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization. Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio.
The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development and analytical development. We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact. The tools that we develop are as different as the teams developing them, and in this Associate Principal Scientist role, the successful candidate will advance the company’s digital-first process development strategy by deploying mechanistic, CFD-based, and data-driven modeling approaches to design, de-risk, and optimize sterile drug substance (DS) and drug product (DP) manufacturing processes. The position will sit at the intersection of first-principles physics, advanced CFD, and machine learning / data science, enabling predictive understanding, robust scale-up,