Scientific Computing Engineer - Drug Product Process Modeling & Data Science

Novartis · Healthcare & Pharma

Hyderabad (Office)
On-site
Posted 12 days ago
Data & AI

This job has closed

Novartis is no longer accepting applications for it. Their other open roles and similar jobs are below.

About the job

Job Description Summary

We are looking for a hands-on Scientific Computing Engineer to strengthen process modeling, statistics, and data science capabilities in Drug Product development. This role is intended for an early-career engineer with a strong quantitative foundation, practical programming skills, and the ability to translate formulation and process questions into data- and model-based solutions.

The ideal candidate has a background in mechanical engineering, process engineering, chemical engineering, or a closely related engineering discipline, and is motivated to work at the interface of pharmaceutical formulation, powder technology, process understanding, statistics, and modern data science.

Experience with AI or machine learning is welcome, but not the primary selection criterion. We expect that a candidate with strong engineering judgment, solid mathematics, and a fast-learning mindset can acquire the required AI methods on the job.

What you will do

You will work hands-on with formulation scientists, process engineers, data scientists, and manufacturing experts to develop practical modeling and analytics solutions for drug product development. The focus is on building useful tools, models, analyses, and workflows that improve process understanding and support decisions from laboratory studies through scale-up.

Job Description

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.

Skills Desired

Biostatistics, Curious Mindset, Data Governance, Data Governance Framework, Data Literacy, Data Science, Data Visualization, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Statistical Analysis, Time Series Analysis