Specialist, Data Science & Artificial Intelligence

Biopharma Careers; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Biopharma Careers seeks an early-career data scientist to advance mechanistic and data-driven modeling for drug development. Build and apply statistical and machine learning methods to solve formulation and process questions with emphasis on transparency and reproducibility.

You will collaborate with cross-functional teams to prepare reports, dashboards, and narratives, and contribute to automation of data workflows in a global setting.

Qualifications

• Master’s degree or PhD in a quantitative engineering discipline.
• 2–4 years of relevant industry experience after a master’s degree, or 0–4 years after a PhD.
• Strong foundation in process science, transport phenomena, statistics, numerical methods, and/or mechanistic modeling.
• Hands-on programming in Python or a similar language.
• Experience applying statistics, DoE, data analysis, simulation, optimization, or ML to engineering problems.
• Ability to work with experimental and industrial datasets, including data cleaning and uncertainty interpretation.
• Strong communication skills to explain technical concepts to non-experts.

Responsibilities

• Develop and apply mechanistic, empirical, statistical, and hybrid modeling to support drug product formulation and process development.
• Translate questions into model- and data-ready statements with defined success criteria and uncertainty considerations.
• Apply statistics, DoE, multivariate analysis, and data-driven modeling to plan experiments and analyze results.
• Build predictive models and decision-support tools for drug product unit operations, focusing on oral solid dosage forms.
• Create end-to-end data science solutions: data prep, exploration, modeling, validation, deployment, and lifecycle management.
• Produce clear visualizations, dashboards, and narratives to communicate insights to diverse stakeholders.
• Contribute to automation and AI-assisted workflows for data prep, modeling, and reporting with scientific oversight.
• Contribute to knowledge sharing, documentation, and reusable modeling assets within global modeling and digital community.

Skills

Python

Statistics

DoE

Data visualization

Uncertainty analysis

Regulatory awareness

Communication

Education

Master’s degree or PhD in quantitative engineering

Tools

PBM

gPROMS

DEM

Digital twins

Job description

• Understand 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. 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.

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.