SME - Computer Scientist / Data Scientist

About the Role

Success Booster is a global engineering research and development services company that connects engineering and digital technologies. We offer design, development, and testing services to enterprises worldwide.

Responsibilities

• Develop physics-based, mathematical, and statistical models to represent real-world systems and processes.
• Apply first-principles modeling, differential equations, probabilistic models, and stochastic processes.
• Build predictive and descriptive models using statistical inference and machine learning techniques.
• Perform uncertainty quantification, sensitivity analysis, and error propagation.
• 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, such as Physics-Informed ML.
• Validate models using rigorous statistical testing and cross-validation.
• Design and run numerical simulations and Monte Carlo experiments.
• Implement model solvers and optimization routines in Python or similar languages.
• Work with numerical methods for ODEs, PDEs, and optimization problems.
• Improve the computational efficiency, stability, and scalability of models.
• Translate complex business or scientific problems into mathematical formulations.
• Conduct exploratory analysis and hypothesis-driven investigations.
• Review academic and industry research to apply relevant methodologies.
• Document assumptions, methodologies, and results clearly and transparently.
• Collaborate closely with domain experts, engineers, and product teams.
• Communicate technical findings to non-technical stakeholders clearly.
• Support decision-making through model-driven insights and scenario analysis.
• Contribute to best practices, documentation, and knowledge sharing.

Preferred Candidate Profile

• Strong foundation in Physics, Applied Mathematics, or Engineering principles.
• Expertise in statistical modeling and inference.
• Experience with numerical methods, optimization, and simulations.
• Solid understanding of probability theory and linear algebra.
• 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.