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:
- 3+ years of experience in machine learning or a similar role, with a strong focus on time series forecasting.
- Proven expertise in machine learning and time series models such as LSTM, TFT, Neural ODEs, and ARIMA.
- Proficiency in programming languages such as Python, R, or Julia, and experience with machine learning libraries like TensorFlow, PyTorch, and GluonTS.
- Experience working with large financial datasets and a solid understanding of financial markets.
- Strong understanding of statistical modeling and machine learning techniques, including regularization and hyperparameter tuning.
- A Master's degree or PhD in Computer Science, Mathematics, Statistics, or a related field, with a focus on machine learning.
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