Computer Scientist - Scientific & Mathematical Computing

Job Summary :We are seeking an experienced SME Computer Scientist with expertise in physics-based modeling, applied mathematics, statistical analysis, and data science to develop advanced analytical solutions for engineering and operational applications. The ideal candidate will combine first-principles modeling, machine learning, and scientific computing to build interpretable and predictive models that support Operations & Maintenance (O&M), asset optimization, and digital engineering initiatives. This role requires strong research, analytical, and computational skills with experience in numerical modeling, simulations, and hybrid AI approaches such as Physics-Informed Machine Learning.Key Responsibilities :- Design and develop advanced analytical models by combining physics-based principles with statistical and machine learning techniques.- Build predictive, simulation, and optimization models for engineering and operational use cases.- Develop digital solutions to support Operations & Maintenance (O&M), asset performance monitoring, and decision support systems.- Apply mathematical modeling, numerical methods, and statistical inference to solve complex real-world engineering problems.- Develop and validate predictive models using time-series analysis, probabilistic methods, and uncertainty quantification.- Design hybrid models that integrate first-principles physics with machine learning and deep learning techniques.- Perform data preprocessing, feature engineering, exploratory data analysis, and model validation on large and complex datasets.- Collaborate with domain experts, software engineers, and data scientists to translate engineering requirements into scalable analytical solutions.- Evaluate model robustness, accuracy, and explainability while ensuring scientific rigor.- Document methodologies, assumptions, model performance, and technical findings for internal and customer stakeholders.- Contribute to research initiatives, proof-of-concepts, and innovation in scientific computing and AI-driven engineering.Required Technical Skills :1. Scientific & Mathematical Computing :- Strong foundation in Physics, Applied Mathematics, Engineering Mathematics, Statistical Analysis, Probability Theory, and Linear Algebra.- Expertise in Numerical Methods, Optimization Techniques, Scientific Simulations, Statistical Modeling, Model Validation, and Uncertainty Quantification.2. Data Science & Machine Learning :- Strong experience developing predictive analytics and machine learning models.- Hands-on experience with Time-Series Analysis, Bayesian Statistics, Stochastic Modeling, Regression and Classification Models, and Feature Engineering.- Experience building interpretable and robust predictive models for real-world datasets.3. Programming & Tools :- Strong programming skills in Python.- Hands-on experience with NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels.- Experience with scientific computing tools such as MATLAB, R, and Simulation Frameworks.- Experience working with Jupyter Notebook and version control tools such as Git. (ref:hirist.tech) Job Summary :We are seeking an experienced SME Computer Scientist with expertise in physics-based modeling, applied mathematics, statistical analysis, and data science to develop advanced analytical solutions for engineering and operational applications. The ideal candidate will combine first-principles modeling, machine learning, and scientific computing to build interpretable and predictive models that support Operations & Maintenance (O&M), asset optimization, and digital engineering initiatives. This role requires strong research, analytical, and computational skills with experience in numerical modeling, simulations, and hybrid AI approaches such as Physics-Informed Machine Learning.Key Responsibilities :- Design and develop advanced analytical models by combining physics-based principles with statistical and machine learning techniques.- Build predictive, simulation, and optimization models for engineering and operational use cases.- Develop digital solutions to support Operations & Maintenance (O&M), asset performance monitoring, and decision support systems.- Apply mathematical modeling, numerical methods, and statistical inference to solve complex real-world engineering problems.- Develop and validate predictive models using time-series analysis, probabilistic methods, and uncertainty quantification.- Design hybrid models that integrate first-principles physics with machine learning and deep learning techniques.- Perform data preprocessing, feature engineering, exploratory data analysis, and model validation on large and complex datasets.- Collaborate with domain experts, software engineers, and data scientists to translate engineering requirements into scalable analytical solutions.- Evaluate model robustness, accuracy, and explainability while ensuring scientific rigor.- Document methodologies, assumptions, model performance, and technical findings for in