<p><p>We are looking for a skilled and motivated Machine Learning Engineer / Data Scientist to join our team.
The ideal candidate should have a strong foundation in machine learning and deep learning, with experience in building end-to-end ML pipelines and hybrid modeling approaches that incorporate both physics-based simulations and data-driven insights.
You will work on high-impact projects involving time series forecasting, predictive modeling, and system simulation to support data-driven decision-making.</p><br/><p><b>Key Responsibilities :</b></p><br/><p>- Design, train, and evaluate machine learning and deep learning models for regression, classification, and forecasting tasks.<br/><br/></p><p>- Build and maintain end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, model training, validation, and deployment.<br/><br/></p><p>- Develop physics-informed or hybrid models by combining domain knowledge with machine learning approaches to simulate and predict system behaviors.<br/><br/></p><p>- Implement and optimize time series forecasting models using statistical techniques, classical ML, and deep learning architectures (e.g., LSTM, Transformer).<br/><br/></p><p>- Collaborate with cross-functional teams to translate business needs into machine learning solutions.<br/><br/></p><p>- Monitor and refine model performance post-deployment to ensure robust, scalable outcomes.</p><br/><p><b>Key Skills Required :</b></p><br/><p>- Strong knowledge of Machine Learning and Deep Learning algorithms and their applications.<br/><br/></p><p>- Proven experience in building and deploying end-to-end ML pipelines.<br/><br/></p><p>- Hands-on experience in physics-based modeling or hybrid approaches combining physical models with data-driven techniques.</p><p><br/></p><p>- Solid understanding of time series forecasting using statistical (ARIMA, ETS), machine learning (XGBoost, RF), and deep learning (RNN, LSTM, Temporal CNN) methods.<br/><br/></p><p>- Experience designing regression and classification models for real-world problems.<br/><br/></p><p>- Proficiency in Python, and libraries like TensorFlow / PyTorch, scikit-learn, pandas, NumPy, statsmodels, etc.<br/><br/></p><p>- Strong problem-solving mindset and ability to work with minimal supervision.</p><br/></p> (ref:hirist.tech)