Data Scientist

• Develop and deploy machine learning and statistical models for forecasting, pattern recognition, and financial analytics. • Work with structured and unstructured datasets including market data, alternative datasets, and event-driven information sources. • Design and maintain scalable data preparation and feature engineering workflows. • Conduct rigorous backtesting, validation, and performance analysis to evaluate model robustness and reduce overfitting risk. • Identify proprietary indicators and predictive features that improve analytical accuracy and signal quality. • Collaborate closely with cross-functional stakeholders to translate technical findings into commercially meaningful insights. • Continuously evaluate data integrity, model assumptions, and research methodologies to maintain high analytical standards.
• Bachelor’s or Master’s degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Physics, Financial Engineering, or a related field. • 2+ years of experience working with large-scale analytical or financial datasets. • Strong foundation in statistics, probability, machine learning, and quantitative analysis. • Familiarity with financial markets, trading concepts, or investment data is advantageous. • Advanced proficiency in Python, including common data science and machine learning libraries (e.g. Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow). • Strong SQL and data querying capabilities. • Experience handling high-volume or real-time datasets is a plus. • Strong critical thinking skills with a detail-oriented and hypothesis-driven approach to problem solving.