Qualifications
- Educational Background
- Bachelor’s or Master’s a quantitative field such as Computer Science, Machine Learning, Statistics, Mathematics, Physics, Engineering, or financial fields such as Finance or Economics.
2. 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.
3. 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.
4. 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.
5. 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.
