We are looking for a motivated financial data scientist to join our Quantitative Research team. The ideal candidate will possess a strong foundation in programming and machine learning, a passion for finance, and a basic understanding of financial principles.

Key Responsibilities:

• Explore, analyze, and implement state-of-the-art research papers on machine learning methodologies for financial time-series forecasting and portfolio optimization.
• Design and develop backtest for ML strategies to assess their performance and robustness.
• Develop interactive dashboards to monitor and analyze performance evaluation criteria for developed strategies.
• Continuously stay updated on advancements in machine learning and quantitative finance.
• Optimize strategies for real-world implementation.

 

Qualifications:


   1-  Technical Expertise

• Strong programming skills in Python,
• Proficiency in machine learning libraries such as PyTorch, scikit-learn, etc.
• Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib or plotly).
• Familiarity with OOP principles and implementation.
• Experience in designing and implementing modular, reusable, and maintainable codebases.
• Familiarity with SQL and/or NoSQL databases (is a plus).
• Proficiency in using Git for version control and collaborative coding. 
• Knowledge of DevOps tools like Docker, Kubernetes, or similar is a plus
• Hands-on experience with backtesting frameworks such as Backtrader or zipline is a plus.

    2- Experience in Machine Learning

• Proven experience in developing and deploying machine learning models (e.g., supervised and unsupervised learning, neural networks, deep learning and reinforcement learning is a plus).
• Knowledge of feature engineering, hyperparameter tuning, and model evaluation techniques.

     3- Quantitative and Financial Knowledge

• Basic foundation in linear algebra, statistics.
• Ability to interpret mathematical models and apply them to practical trading strategies.
• Knowledge of financial markets (e.g., equities, derivatives, fixed income) and modern portfolio theory.
• Familiarity with backtesting, and strategy evaluation.

     4-Problem-Solving and Research Skills

• Strong ability to identify, analyze, and solve challenging problems independently.
• Enthusiasm for learning and staying updated with the latest trends in machine learning and finance.