Scientific Computing Engineer - Drug Product Process Modeling & Data Science

Major Accountabilities

• Develop and apply mechanistic, empirical, statistical, and hybrid modeling approaches to support drug product formulation and process development, especially for process understanding, scale-up, and manufacturing-relevant questions.
• Translate formulation and process questions into model- and data-ready problem statements; define success criteria, assumptions, and uncertainty considerations with subject-matter experts.
• Apply statistics, Design of Experiments, multivariate analysis, and data-driven modeling to plan experiments, analyze results, and accelerate learning cycles.
• Build predictive models and decision-support tools for key drug product unit operations, with particular interest in oral solid dosage forms, powder technology, formulation, and process engineering.
• Build end-to-end data science solutions including data preparation, exploratory analysis, modeling, validation, deployment, and lifecycle management, with a focus on transparency and reproducibility.
• Create clear visualizations, dashboards, and technical narratives to communicate insights and support decision making for diverse stakeholders.
• Contribute to automation and AI-assisted workflows for data preparation, modeling, analysis, and reporting, while maintaining scientific oversight and practical usability.
• Contribute to knowledge sharing, documentation, internal standards, and reusable modeling/AI assets within the global modeling and digital community.

Essential Skills

• Master’s degree or PhD in mechanical engineering, process engineering, chemical engineering, pharmaceutical engineering, materials science, applied mathematics, statistics, data science, or a closely related quantitative engineering discipline.
• Early-career profile preferred, typically with 2–4 years of relevant industry experience after a master’s degree or 0–4 years after a PhD, and a clear motivation for hands-on modeling, coding, and applied problem solving.
• Core skills
• Strong engineering and mathematical foundation, including process science, transport phenomena, statistics, numerical methods, and/or mechanistic modeling.
• Must have hands-on programming experience in Python or a similar programming language, with the ability and motivation to become productive in Python very quickly if not already fluent.
• Experience applying statistics, DoE, data analysis, simulation, optimization, and/or machine learning to engineering or scientific problems.
• Ability to work with experimental and industrial datasets, including data cleaning, exploratory analysis, and uncertainty-aware interpretation including model credibility assessments according to regulatory guidelines & standards.
• Strong communication skills to explain technical concepts to non-experts and influence decisions.
• Digital & AI capabilities (beneficial; can be developed on the job)
• Basic experience with machine learning, model evaluation, or AI-enabled analytics is an advantage, but less important than strong engineering fundamentals, coding ability, and learning agility.
• Interest in AI-assisted modeling, automation, and agent-based workflows, with willingness to learn and apply these methods in a scientifically rigorous way.
• Understanding of model lifecycle management, reproducibility, and deployment considerations in regulated environments.
• Experience with visualization and storytelling, such as dashboards or clear technical reporting.

Desirable Skills

• Experience or academic exposure to powder technology, formulation science, oral solid dosage forms, pharmaceutical unit operations, process modeling tools, PBM, DEM, gPROMS, or digital twins.
• Exposure to QbD principles, PAT concepts, or regulatory-relevant modeling activities.
• Experience working in global matrix organizations.