AI-enabled Scientific Computing Expert - Drug Product Development at Novartis

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

16 Oct, 26

Salary

0.0

Posted On

18 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Artificial Intelligence, Data Science, Machine Learning, Python, R Programming, Statistical Analysis, Biostatistics, Deep Learning, Data Visualization, SQL, Time Series Analysis, Design of Experiments, Digital Twins, Process Modeling, Drug Product Development, Stakeholder Engagement

Industry

Pharmaceutical Manufacturing

Description

Job Description Summary We are looking for an AI-enabled Scientific Computing Expert to strengthen Drug Product development capabilities within a global CMC organization. In this role, you will focus on how AI, data science, and modeling can improve decision-making in CMC, from early formulation development to commercial manufacturing. You will work in a highly interdisciplinary environment, collaborating with formulation scientists, process engineers, data scientists, and manufacturing experts to advance model-informed decision-making and embed AI-native workflows into day-to-day development activities. Rather than applying models in isolation, you will design AI-native workflows where mechanistic understanding, data-driven models, and human expertise work together. Job Description Major Accountabilities: You will combine process and product understanding with AI and advanced analytics to deliver decision support across the drug product lifecycle. The focus is on practical impact through AI-supported product development—modeling, experimentation, and knowledge management. · Apply mechanistic, empirical, statistical, and hybrid (physics + machine learning) modeling approaches to support drug product formulation and process development from early lab phase through scale-up and commercialization. · Translate formulation and process questions into model- and data-ready problem statements; define success criteria, assumptions, and uncertainty considerations with subject-matter experts. · Use AI and advanced analytics to guide experimentation (e.g., model-based Design of Experiments, Bayesian Optimization), accelerate learning cycles, and continuously refine models as new data becomes available. · Develop predictive models, digital twins, and decision-support tools for key drug product unit operations (e.g., oral solid dose manufacturing). · Build end-to-end data science solutions (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/agent-based workflows for data preparation, modeling, analysis, and reporting - improving efficiency while maintaining scientific oversight. Contribute to knowledge sharing, documentation, internal standards, and reusable modeling/AI assets within the global modeling and digital community Minimum Requirements Master’s degree or PhD in chemical engineering, pharmaceutical sciences, mechanical engineering, materials science, physics, applied mathematics, statistics, data science, or a related quantitative discipline. Experience or strong interest in pharmaceutical development and manufacturing processes or other complex process environments. Solid understanding of transport phenomena, process science, and/or statistical modeling principles. Hands-on experience with programming and data analysis (primarily Python; R is a plus).Experience applying statistics, DoE, multivariate analysis, and/or machine learning in scientific or industrial settings. Experience using or developing machine learning models (including model evaluation and validation). Familiarity with AI-assisted modeling, automation, and/or agent-based workflows. Understanding of model lifecycle management, reproducibility, and deployment considerations in regulated environments. Experience with visualization and storytelling (e.g., dashboards or clear technical reporting). Desirable Requirements: Experience with pharmaceutical process modeling tools (e.g., PBM, gPROMS, DEM tools) and/or digital twins. Strong communication skills to explain technical concepts to non-experts and influence decisions. Exposure to Qods principles, PAT concepts, or regulatory-relevant modeling activities. Experience working in global matrix organizations. 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 & standard. Skills Desired Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Logistic Regression Model (Inactive), Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis

Responsibilities

Develop AI-native workflows and predictive models to improve decision-making in drug product formulation and commercial manufacturing. Collaborate with interdisciplinary teams to implement digital twins and advanced analytics across the drug product lifecycle.