A leading hedge fund specializing in Quantitative Research and Trading is looking to onboard a Machine Learning Researcher. The firm leverages cutting-edge technology and data science to drive superior performance in financial markets. As they continue to expand our team, they are seeking a talented and highly motivated Machine Learning Researcher to join our Quantitative Trading Strategies team.
Algorithmic Research and Development:
Conduct thorough research to develop, optimize, and implement machine learning models for quantitative trading strategies.
Collaborate with a cross-functional team to enhance existing algorithms and develop new models that exploit market inefficiencies.
Data Analysis and Feature Engineering:
Work with large financial datasets to identify relevant features and signals that can be incorporated into trading models.
Conduct statistical analysis to evaluate the predictive power of potential features and continuously refine models based on performance metrics.
Model Testing and Validation:
Design and implement rigorous testing procedures to validate the performance of machine learning models under various market conditions.
Conduct backtesting and stress testing to ensure robustness and reliability of trading strategies.
Collaborate with risk management teams to ensure that machine learning models adhere to predefined risk parameters.
Develop and implement risk mitigation strategies to safeguard the fund's capital.
Stay Abreast of Industry Developments:
Keep up-to-date with the latest advancements in machine learning, quantitative finance, and market micro-structure to incorporate cutting-edge techniques into our trading strategies.
Advanced degree (Ph.D. or Master's) in a quantitative field such as Computer Science, Statistics, Mathematics, or related discipline.
4+ years of experience in applying machine learning techniques to financial markets, particularly in algorithmic trading.
Proficiency in programming languages such as Python, or C++.
Strong quantitative and analytical skills, with a deep understanding of machine learning models, statistical modeling and time series analysis.
Experience with financial data, including tick data, order book data, and market micro-structure.
Experience with deep learning frameworks such as TensorFlow or PyTorch.
Knowledge of market micro-structure and algorithmic trading strategies.
Strong communication skills and the ability to convey complex technical concepts to non-technical stakeholders.