Description:
As a Machine Learning Engineer, you will develop and deploy machine learning models that drive our trading algorithms and investment strategies. You’ll work closely with quantitative researchers, portfolio managers, and data engineers to deliver scalable, real-time solutions that support high-frequency trading and risk management.
Key Responsibilities:
- Design, develop, and optimize machine learning models for trading, risk analysis, and market forecasting.
- Collaborate with quantitative researchers to integrate data-driven insights into investment strategies.
- Work with large datasets and implement robust data pipelines to support machine learning workflows.
- Deploy and monitor models in production environments, ensuring performance and scalability.
- Continuously improve model accuracy through testing, tuning, and retraining techniques.
- Analyze market trends and develop algorithms that optimize portfolio management.
Skills & Qualifications:
- Master’s or Ph.D. in Computer Science, Mathematics, Engineering, or a related field.
- Strong experience in machine learning, deep learning, and data analysis.
- Proficiency in programming languages such as Python, C++, or Java.
- Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience working with financial data and algorithmic trading systems.
- Strong understanding of data structures, algorithms, and statistical modeling.
- Experience in deploying models in cloud environments (AWS, GCP, etc.) is a plus.