Data Scientist

What you will do:

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.

What you will need:

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.