About Us:

At Dr. Reddy’s Laboratories, we believe Good Health Can’t Wait. With a presence in 66 countries and a team of over 24,000 passionate professionals, we’re on a mission to reach 1.5 billion+ patients by 2030. We’re driven by science, powered by purpose, and committed to sustainability. Join us in shaping the future of affordable and innovative healthcare.

Key Responsibilities & Technical Deliverables
As Head of M&S you will:

• Define and drive the strategic roadmap for modelling & simulation within product development (APIs, formulation, scale-up/tech transfer, manufacturing) and process engineering. The expertise would be required for M&S at a unit operation / process as well at a systems level.
• Foster a culture of innovation: Scan emerging technologies (digital twin, AI/ML integration with mechanistic models, in-silico trials, hybrid modelling) and lead pilot/proof-of-concept initiatives to embed next-generation modelling capabilities.
• Oversee development, validation and deployment of advanced simulation models for complex unit operations
• Champion digital transformation by implementing modelling tool-chains (e.g., CFD, DEM, population balance models, hybrid mechanistic/data-driven models, digital twin frameworks) in key process development, scale-up and tech-transfer workflows.
• Partner closely with cross-functional teams: API R&D, Formulation R&D, MSAT, Process Engineering, Quality, Regulatory, Manufacturing, Digital & Data to translate real-world process challenges into modelling/virtual experiments, deliver data-driven insights and drive decisions (e.g., reduce scale-up failures, improve robustness, shorten timeline).
• Define and monitor M&S Key Performance Indicators (KPIs) such as: reduction in scale-up time, reduction in change orders, number of virtual experiments vs physical, increase in first-pass success rate, cost/time savings, number of projects using digital twin/hybrid modelling.
• Ensure that all modelling initiatives support regulatory-compliant workflows (e.g., design space justification, QbD, tech transfer documentation, risk-based justification) and align with data integrity, GMP, and cost-effectiveness mandates.
• Communicate effectively: translate complex technical modelling results into clear business-impact narratives, present to senior stakeholders, and influence strategic decisions on product/process development, CAPEX planning and technology investments.

Education & Experience:

• PhD (preferred) or MTech/BTech in Chemical Engineering, Process Engineering, Computational Science, Mechanical Engineering, or related discipline.
• Minimum of 15 years industrial experience in pharmaceutical or chemical process modelling; with at least 5 years in a senior leadership role managing modelling/simulation teams and projects.
• Proven track record delivering modelling & simulation capabilities in pharmaceutical product/process development, continuous manufacturing or scale-up environment.

Technical Expertise:

• Deep expertise in modelling methods: Computational Fluid Dynamics (CFD), Discrete Element Method (DEM), Population Balance Models (PBM), first-principles mechanistic models, digital twin development, hybrid modelling (mechanistic + data-driven).
• Strong experience with simulation platforms/tools (e.g., ANSYS/Fluent, STAR-CCM+, gPROMS, Aspen, MATLAB/Simulink, Python, R, computational libraries), HPC environments, model validation and verification processes.
• Familiarity with formulation/process modelling: IVIVC/IVIVR, dissolution modelling, mechanistic absorption/distribution modelling (for drug-product development), unit-operation modelling (granulation, tableting, coating, fluid-bed), continuous manufacturing. Industry sources highlight the role of modelling & simulation across development stages. (Contract Pharma)
• Ability to integrate modelling outputs with project decision-making: risk assessment, design space definition, troubleshooting, scale-up/tech-transfer support, regulatory submission support.
• Experience in CAPEX planning and software license management/strategy, budgeting for modelling infrastructure, building modelling centres of excellence.