Data Science Analyst

Про роль

This role exists because the platform's analytical outputs require continuous empirical scrutiny. The Monte Carlo simulations, the Value-at-Risk computations, and the cross-listing reconciliation each rest on assumptions that must be calibrated, validated, and revisited. The successful candidate will own the analytical methodology behind these outputs and will be the principal author of the Methodology documentation as it evolves.

Обов'язки

• Calibrate and validate the scenario modelling and risk computation engines.
• Conduct the empirical research that underpins the platform's cross-listing analytics and other quantitative features.
• Author and maintain the Methodology documentation as the analytical apparatus evolves.
• Collaborate with the engineering team on the implementation of new analytical features.
• Contribute to Drusus Insights with empirical research pieces.

Кого ми шукаємо

• A postgraduate degree in a quantitative discipline (statistics, economics, financial engineering, applied mathematics, physics).
• Strong proficiency in Python, R, or comparable analytical languages; comfortable with the standard scientific stack (NumPy, Pandas, scikit-learn).
• Familiarity with financial time-series and the standard analytical apparatus of quantitative finance.
• Capability to communicate analytical reasoning clearly in writing, in a register suitable for the Insights archive.

Корисно, але не обов'язково

• Prior research or industry experience in quantitative finance.
• Familiarity with the academic literature on Value-at-Risk and Monte Carlo methods.