Advisor/Senior Advisor

About the Role

The Advisor/Senior Advisor – Process Engineering and Modeling will provide technical leadership in modeling, simulation, data science, and process engineering to support small molecule and synthetic peptide drug substance development. The role applies deep scientific, engineering, mathematical, and computational expertise to solve problems in chromatography purification, reaction systems, and other unit operations at both small and large scale. The successful candidate will demonstrate strong technical depth, broad modeling capability, scientific leadership, and a collaborative approach to solving complex process development and manufacturing challenges.

Key Responsibilities

• Lead modeling activities for small molecule and synthetic peptide drug substance development, including mechanistic modeling, data-driven modeling, simulation, and optimization.
• Design and develop model-derived solutions to support clinical and commercial manufacturing processes, including batch and continuous processes, material supply, registration-enabling process understanding, and robust control strategies.
• Identify and communicate process risks, design opportunities, and modeling-based recommendations to project teams and management to enable sound technical and business decisions.
• Use internal and external modeling and experimental resources to mitigate technical risks before they affect project delivery timelines.
• Partner with product teams to provide technical oversight for process scale-up and technology transfer to internal manufacturing sites and external CDMOs.
• Collaborate across PRD to identify decisions that can be informed by data science, statistical modeling, machine learning, and process engineering analysis.
• Select and apply fit-for-purpose data science tools and methodologies to influence development decisions.
• Develop tools and algorithms to mine patterns in existing data and generate recommendations for future experiments, process questions, and modeling strategies.
• Communicate model assumptions, limitations, results, and recommendations clearly to scientific, engineering, manufacturing, and leadership stakeholders.
• Mentor and influence project teams by promoting model-informed development, scientific rigor, and an Agile mindset.

Requirements & Tech Stack

• Develop and apply customized computer models, computational tools, and commercial modeling software to support process understanding and decision-making.
• Use modeling and simulation to inform process development, scale-up, technology transfer, manufacturing, and validation.
• Apply artificial intelligence and machine learning methods to solve process engineering problems and extract insight from complex data sets.
• Contribute to research projects and programs focused on development of small molecule and synthetic peptide active pharmaceutical ingredients for clinical and commercial use.
• Design data-rich experiments using automated laboratory reactors, process analytical technology probes, and appropriate kinetic, thermodynamic, and transport measurements to support impurity control and manufacturing scale-up.
• Improve research and development effectiveness through computer modeling, simulation, optimization, and data-driven decision-making.

Qualifications

• Ph.D. in Chemical Engineering or a closely related discipline.
• 0–5 years of industry experience in modeling, simulation, process engineering, or data science applied to pharmaceutical process development.
• Substantial computer modeling expertise, including application and/or development of modeling tools for process engineering problems.
• Demonstrated experience with process development, scale-up, and technology transfer to internal manufacturing sites or external CDMOs.
• Strong foundation in mathematical analysis, advanced mathematics, and applied problem solving for science- and engineering-based systems.
• Proficiency with mathematical analysis and process modeling programs such as JMP, MATLAB, ASPEN, Dynochem, or gPROMS.
• Experience programming in scripting languages such as R or Python.
• Experience using statistical or machine learning tools, with the ability to implement custom algorithms as needed.
• Strong interpersonal and communication skills, with the ability to collaborate across functions, influence technical decisions, and drive solutions with an Agile mindset.

Preferred Qualifications

• Experience applying optimization, computational fluid dynamics, and computational chemistry tools to solve process engineering problems.
• Experience with chromatography purification experimentation and modelling.
• Experience designing data-rich experiments using automated lab reactors and process analytical technology probes.
• Experience translating kinetic, thermodynamic, and transport data into process understanding, impurity control strategies, and scale-up recommendations.
• Successful interaction with senior engineering and scientific personnel across matrixed project teams.

Additional Information

• Limited international travel, approximately 2–4%, may be required.
• Work-hour flexibility is required to support collaboration across multiple time zones.
• Location: Bengaluru, India.