Scientific Computing Engineer - Drug Product Process Modeling & Data Science
Novartis (company site, opens in a new tab)2–4 yrsHyderabadOn-siteDirect career pageEst. 12–22 LPA
Posted on 8 Sept
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Job description
Structured overview of role & requirements
About This Role
- Develop and apply mechanistic, empirical, and hybrid models to support drug product formulation, process development, scale-up, and manufacturing questions.
- Build end-to-end data science solutions including data preparation, modeling, validation, deployment, and lifecycle management with transparency and reproducibility focus.
- Create visualizations, dashboards, and technical communication to support decision making, and contribute to automation and AI-assisted workflows in modeling and reporting.
Minimum Requirements
- Master’s degree or PhD in mechanical, chemical, pharmaceutical engineering, materials science, applied mathematics, statistics, data science, or closely related quantitative engineering discipline.
- 2–4 years of relevant industry experience after master’s or up to 4 years after PhD (early-career profile).
- Hands-on programming experience in Python or similar language; strong engineering and mathematical foundation including process science, transport phenomena, statistics, numerical methods, or mechanistic modeling.
- Experience applying Design of Experiments, statistics, data analysis, simulation, optimization, and/or machine learning to engineering or scientific problems; ability to work with experimental and industrial datasets including data cleaning and uncertainty assessment.
Ideal Candidate Profile
- Early-career candidate with motivation for hands-on modeling, coding, and applied problem solving in drug product process modeling.
- Strong foundation in engineering/math combined with programming skills, with interest in AI-assisted modeling and automation in regulated environments.
- Capable of translating complex formulation and process questions into model-ready problem statements and communicating technical insights to diverse stakeholders.