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.

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.