Core Technical Skills
- Robust foundation in Physics, Applied Mathematics, or Engineering principles
- Expertise in statistical modeling and inference
- Develop physics‑based, mathematical, and statistical models to represent real‑world systems and processes
- Experience with numerical methods, optimization, and simulations
- Solid understanding of probability theory and linear algebra
Modeling & Analytics
- Experience with time‑series analysis, stochastic modeling, or Bayesian methods
- Knowledge of model validation, uncertainty analysis, and robustness testing
- Exposure to physics‑informed modeling or hybrid (physics + ML+Deep Learning) approaches
- Ability to work with noisy, incomplete, or real‑world data
- Analyze large, complex datasets to extract patterns, trends, and insights
- Apply regression, classification, time‑series analysis, Bayesian methods, and optimization
- Integrate physics‑informed or constraint‑based approaches with ML models (e.g., Physics‑Informed ML)
Role will be discussed during interview discussion.