Scientific Computing Engineer - Drug Product Process Modeling & Data Science

Novartis India; Hyderabad; On-site; INR 1,400,000 - 2,100,000; Full time

Job summary

Novartis India is seeking a data science professional to support drug product formulation and process development through mechanistic and data-driven modeling. You will build predictive models, perform statistical analyses, and develop end-to-end data science solutions with transparent workflows.

Responsibilities include translating formulation questions, designing experiments, and communicating insights through dashboards and technical narratives to diverse stakeholders in a globally matrix

Qualifications

• Master’s degree or PhD in mechanical/chemical/pharmaceutical engineering, mathematics, or data science.
• 2–4 years after master’s or 0–4 years after PhD with hands-on modeling and coding.
• Strong foundation in process science, transport phenomena, statistics, numerical methods, or mechanistic modeling.

Responsibilities

• Develop and apply modeling approaches for drug product formulation and process development.
• Translate questions into model/data-ready statements with SMEs.
• Apply statistics, DoE, and multivariate analysis to plan experiments and accelerate learning.
• Build predictive models and decision-support tools for unit operations, esp. oral solid dosage and powder tech.
• Develop end-to-end data science solutions from data prep to deployment with reproducibility.
• Create visualizations and narratives to communicate insights to stakeholders.
• Contribute to AI-assisted workflows and knowledge sharing across teams.

Skills

Programming

Statistics

Data analysis

Communication

Modeling

Automation

Education

Master’s degree or PhD in engineering or quantitative field

Tools

Python

gPROMS

DEM

PBM

Digital twins

Job description

• Understands business problems, supports an integrated analytical approach to mine data sources, manage data effectively, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights, and automate processes for reducing effort and time for repeated use.
• Provide data science and quantitative analytical support by ensuring the timely delivery of high-quality reports.
• Build capabilities to leverage the Science of data.

Summary

• Understands business problems, supports an integrated analytical approach to mine data sources, manage data effectively, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights, and automate processes for reducing effort and time for repeated use.
• Provide data science and quantitative analytical support by ensuring the timely delivery of high-quality reports.
• Build capabilities to leverage the Science of data.

About The Role

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.

Why Novartis:

Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Benefits and Rewards:

Learn about all the ways we’ll help you thrive personally and professionally.

Read our handbook (PDF 30 MB)