Scientific Computing Engineer - Drug Product Process Modeling & Data Science

Biopharma Careers; Hyderabad; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Biopharma Careers is seeking an early‑career data scientist to develop mechanistic and data‑driven models for drug product formulation and process development. You will translate questions into model-ready problems, apply DOE and multivariate methods, and build predictive tools for unit operations with a focus on transparency and reproducibility.

You will create clear visualizations and dashboards to communicate insights to diverse stakeholders and support automation and AI‑assisted workflows

Qualifications

• 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 2–4 years after master’s or 0–4 years after PhD.

Responsibilities

• Develop/mechanistic, empirical, statistical, and hybrid models for formulation and process development.
• Translate questions into model-ready statements and define success criteria with SMEs.
• Apply statistics, DoE, multivariate analysis, and data-driven modeling to plan experiments and accelerate learning.
• Build predictive models and decision-support tools for drug product unit operations, especially oral solids.
• Create end-to-end data science solutions with emphasis on transparency and reproducibility.
• Develop clear visualizations and narratives to communicate insights to diverse stakeholders.
• Contribute to automation and AI-assisted workflows while ensuring scientific oversight.

Skills

Engineering fundamentals

Python programming

Statistics & DoE

Data analysis

Communication

AI & digital capabilities

Model lifecycle

Visualization & dashboards

Education

Master’s or PhD in quantitative engineering

Tools

PBM

gPROMS

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.

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.