Machine Learning Engineer – Time Series Forecasting

We are seeking a Machine Learning Engineer to develop advanced time series forecasting models that support data-driven decision-making in financial markets. The ideal candidate will apply modern machine learning algorithms and techniques to solve complex forecasting problems, ensuring accuracy and robustness in rapidly changing environments.

Responsibilities :

• Design, develop, and optimize machine learning models for time series forecasting, focusing on financial data such as stock prices, economic indicators, and market behaviors.
• Leverage state-of-the-art machine learning techniques such as LSTM (Long Short-Term Memory) networks, Temporal Fusion Transformers (TFT) , Neural ODEs , and DeepAR to enhance forecasting performance.
• Apply methods to reduce overfitting , including cross-validation , regularization , and model fine-tuning to ensure robust, generalizable models.
• Perform backtesting and validation of models using historical financial data to ensure accuracy and consistency across various market conditions.
• Collaborate with data scientists and financial analysts to integrate forecasting models into production systems for real-time decision-making.
• Use advanced techniques like multivariate time series analysis , regime-switching models , and hierarchical forecasting to improve performance across various markets.
• Apply advanced techniques, including regularization methods , cross-validation , and dropout , to prevent overfitting in time series forecasting models, ensuring robust and generalizable predictions across different market conditions.
• Continuously explore new research and technologies in machine learning to improve forecasting capabilities and adapt models to new data.

Requirements and Skills :

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