Quantitative Finance Analysts

Track A – Quantitative Finance / Portfolio Analytics Intern

Job duties:

• Perform quantitative research and data‑driven analysis on global markets, asset classes and investment strategies, using large financial datasets and statistical techniques to generate actionable investment insights.

• Help develop, backtest and enhance systematic trading and portfolio construction models, including factor models, risk models and scenario analysis tools.

• Support client portfolio construction, performance attribution and risk monitoring, analysing exposures, drawdowns, stress‑test results and adherence to investment guidelines.

• Build and maintain research code, analytics libraries and dashboards in languages such as Python/R, following good practices in version control, documentation and testing.

• Work closely with portfolio managers, quantitative developers and other stakeholders to translate business questions into analytical projects and communicate findings clearly in presentations and reports.

• These duties intentionally include keywords like quantitative research, data‑driven analysis, systematic strategies, portfolio optimisation, risk management and stakeholder communication, which align with common internship and analyst descriptions at global investment banks and buy‑side firms.

Track B – Front‑End / Back‑End Fintech Development Intern

Job duties:

• Design, develop and maintain production‑grade web applications and services that support investment analytics, portfolio management and risk reporting for institutional clients.

• Implement front‑end user interfaces (e.g. in JavaScript/React) or back‑end services and REST-ful APIs (e.g. in Java/C++/SQL), focusing on reliability, performance, scalability and security.

• Collaborate with quantitative analysts and product owners to translate business and trading requirements into technical specifications, prototypes and robust software components.

• Contribute to code reviews, testing, debugging and production support, helping to ensure high code quality, adherence to best practices and stable releases.

• Work with large datasets and data pipelines, helping to integrate market, portfolio and risk data into the platform and improve data quality, monitoring and automation.