Key Responsibilities:
Design and implement predictive and regression models on large datasets, leveraging both tree-based methods and deep learning techniques.
Build and maintain robust data integration pipelines in Python.
Apply advanced ML/AI approaches to generate, clean, and validate proprietary datasets.
Conduct backtesting, validation, and performance evaluation of data-driven models.
Collaborate with cross-functional teams to translate research insights into practical solutions.
Continuously monitor and refine models to improve accuracy, scalability, and efficiency.
Qualifications and Experience:
6+ years of hands-on experience in Machine Learning, Data Science, or Quantitative Research.
Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
Solid understanding of statistical modeling, regression, and time-series analysis.
Experience designing and deploying predictive and deep learning models.
Strong analytical and problem-solving skills, with the ability to work independently.
Excellent verbal and written communication skills in English.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Good to Have:
Experience in quantitative finance, trading, or investment-related projects.
Familiarity with systematic strategies, risk modeling, or portfolio optimization.
Exposure to cloud platforms (AWS, GCP, Azure) and large-scale data infrastructure.
Knowledge of financial data APIs, Bloomberg/Refinitiv, or alternative datasets.