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:

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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