Data Science Intern

• 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: 08:00 - 17:00, Monday to Friday, with 1.5 hours lunch break (Interns can have flexible working hours to accommodate their class schedules).

• We are seeking a Data Scientist with strong quantitative thinking and hands-on modeling ability to transform data into reliable analytical and predictive systems.
• Solid grounding in Mathematics and Statistics (linear algebra, probability, inference, optimization). Experience with end-to-end data workflows: data processing, exploration, feature design, and validation.
• Practical experience in building ML models for prediction and pattern discovery (regression, classification, clustering).
• Understanding of model robustness: cross-validation, error analysis, and metric-driven evaluation.
• Familiarity with tree-based models (Random Forest, Gradient Boosting) and regularization techniques.
• Basic knowledge of neural networks; exposure to PyTorch or TensorFlow preferred.
• Experience handling structured datasets and temporal/sequential data.
• Strong proficiency in Python and data ecosystem tools (pandas, NumPy, scikit-learn).
• Ability to synthesize insights from data and communicate findings clearly and concisely.
• Strong problem-solving mindset with attention to data integrity and modeling assumptions.