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.
Dear AllKey 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. ...
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.
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