Machine Learning Engineer – Time Series Forecasting

2 days ago

Miami FL United States Quantision Full time

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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