Advisor/Senior Advisor – Process Engineering and Modeling

Overview

Provide technical leadership in modeling, simulation, data science, and process engineering to support small molecule and synthetic peptide drug substance development, including chromatography purification, reaction systems, and other unit operations at small and large scale.

What you'll do

• 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.
• 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.
• Design data-rich experiments using automated laboratory reactors, process analytical technology probes, and kinetic, thermodynamic, and transport measurements to support impurity control and manufacturing scale-up.

What you'll need

• 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.

Nice to have

• 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.

Details

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