We are seeking a highly motivated and detail-oriented Data Science Intern to join our team. The ideal candidate will be responsible for designing, optimizing, and maintaining end-to-end models that analyze and forecast price movements based on financial data. You will contribute to building data-driven investment strategies using technical analysis and advanced techniques in machine learning (ML) and deep learning (DL).

Key Responsibilities:

- Supporting senior in research, design, and implement predictive models to analyze financial time series data

- Try to develop and backtest systematic trading strategies based on technical indicators, ML/DL models, and other data-driven approaches

- Optimize model performance through feature engineering, hyperparameter tuning, and robust evaluation techniques

- Supporting senior in maintain, update, and deploy ML/DL pipelines to ensure stability and adaptability in live trading environments

- Collaborate with data engineers and developers to ensure smooth integration of research models into production systems

- Monitor and refine strategies based on real-world performance and changing market conditions

- Stay up to date with the latest research and techniques in quantitative finance, ML/DL, and algorithmic trading

Working Hours: 08:30 AM - 17:30 PM, Monday to Friday, with 1,5 hours lunch break (Interns can have flexible working hours to accommodate their class schedules).

An ideal candidate should have a strong foundation in Machine Learning, Deep Learning, and Mathematics, including:

- Strong foundation in mathematics, covering: Linear Algebra, Calculus, Probability, Statistics, and Optimization

- Solid understanding of core ML concepts: Regression, classification, clustering, model evaluation & optimization, data preprocessing

- Familiarity with ensemble methods (bagging, boosting) and regularization techniques (L1/L2)

- Basic knowledge of neural networks and practical experience with PyTorch

- Familiarity with deep learning architectures: RNNs, LSTMs, CNNs

- Experience working with time series data, including preprocessing, feature extraction, and modeling

- Proficient in Python, with hands-on experience using ML/DL libraries such as scikit-learn, TensorFlow, and PyTorch

- Strong analytical thinking and problem-solving skills

Nice to Have:

- Knowledge of portfolio optimization, risk management, or market microstructure

- Familiarity with cloud-based deployment or real-time data processing

- Participation in quantitative competitions (e.g., Kaggle, Numerai)