Data Science Analyst

About the Role

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.

Responsibilities

• 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.

What We Are Looking For

• 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.

Helpful but Not Required

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