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Dr. Nirmal Parmar

Director - Data Analytics & AI · Novartis  |  Research Scientist  |  Founder of Machine Gnostics

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Dr. Nirmal Parmar

Professional Summary

Director - Data Analytics & AI at Novartis with 10+ years of experience leading AI-driven digital transformation across international teams. Founder of Machine Gnostics — the world's first non-statistical machine learning framework. Research scientist specialising in thermal engineering and non-statistical AI, with a joint international PhD across three countries and 20+ peer-reviewed publications.

Current role

  Director - Data Analytics & AI — Novartis

  • Enterprise-wide AI adoption strategy
  • Leading diverse international cross-functional teams
  • Implementing AI solutions in manufacturing and engineering
  • End-to-end digital transformation project management

Education

🇨🇿 🇫🇷   Joint International PhD — Chemical Engineering & Advanced Data Analytics
UCT Prague  ·  University of Tours, France  ·  Czech Academy of Sciences, Prague
🇩🇪   Research Intern — Engineering
University of Wismar, Germany
🇨🇿   Master's — Applied Mechanics
Czech Technical University, Prague
🇮🇳   Master's — Thermal Engineering
Gujarat Technological University, India
🇮🇳   Bachelor's — Mechanical Engineering
Veer Narmad South Gujarat University, India

Core expertise

  AI & Machine Learning

  • Generative AI (GenAI) & LLM implementation
  • Custom neural networks & deep learning
  • MLOps, LLMOps & AIOps
  • Time series forecasting

  Leadership

  • AI strategy development
  • Digital transformation leadership
  • Cross-cultural team management
  • Innovation management

  Thermal Engineering

  • Energy storage systems
  • Renewable engineering
  • Heat transfer applications
  • Thermodynamics

  Mathematical Gnostics

  • Non-statistical AI frameworks
  • Uncertainty analysis
  • Open source leadership
  • Research–industry bridge

Machine Gnostics

World's First Non-Statistical ML Library

"Redesigning the Mathematical Core of AI"

Machine Gnostics is the world's first machine learning library built on non-statistical principles — encoding geometry, physics, and entropy directly into algorithms. It delivers reliable results even from small, noisy, real-world data where conventional statistical AI fails.

Explore Machine Gnostics

"Transforming organisations through intelligent AI adoption — combining rigorous scientific methodology with innovative engineering solutions."

— Dr. Nirmal Parmar