Dear All,

Key Responsibilities • Design, train, and evaluate machine learning and deep learning models for regression, classification, and forecasting tasks. • Build and maintain end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, model training, validation, and deployment. • Develop physics-informed or hybrid models by combining domain knowledge with machine learning approaches to simulate and predict system behaviors. • Implement and optimize time series forecasting models using statistical techniques, classical ML, and deep learning architectures (e.g., LSTM, Transformer). • Collaborate with cross-functional teams to translate business needs into machine learning solutions. • Monitor and refine model performance post-deployment to ensure robust, scalable outcomes.
Key Skills Required • Strong knowledge of Machine Learning and Deep Learning algorithms and their applications. • Proven experience in building and deploying end-to-end ML pipelines. • Hands-on experience in physics-based modeling or hybrid approaches combining physical models with data-driven techniques. • Solid understanding of time series forecasting using statistical (ARIMA, ETS), machine learning (XGBoost, RF), and deep learning (RNN, LSTM, Temporal CNN) methods. • Experience designing regression and classification models for real-world problems. • Proficiency in Python, and libraries like TensorFlow / PyTorch, scikit-learn, pandas, NumPy, statsmodels, etc. • Strong problem-solving mindset and ability to work with minimal supervision.