Quantitative Research Analyst

Education/Experience and Skills Requirement:

• Master’s in mathematics, statistics, physics or financial engineering
• 1-4 years of experience in MTM investigations, quantitative analysis, VBA automation, etc.
• Experience in options and derivatives pricing, as well as calculations related to financial product life cycle
• Strong proficiency in Microsoft Excel, including advanced formulae, data analysis and financial modelling
• Solid programming skills in VBA and Python, with ability to automate processes and build analytical tools
• Quantitative and analytical-problem-solving abilities
• Ability to think creatively and offer out-of-the-box solutions for complex problems
• Strong understanding of system workflows, calculation logic and data analysis techniques
• Ability to work rigorously from specifications while driving investigations and solutions independently
• Strong communication and stakeholder management skills, with ability to explain technical findings clearly
• Collaborative mindset, with ability to work effectively across technology, quantitative and business teams
• Knowledge of Java (advantageous but not mandatory)
• Experience in financial markets, valuation systems, risk systems or quantitative research environments (preferred)

Responsibilities:

• Replicate and analyse financial deals and calculations to identify discrepancies and valuation issues
• Perform calculation replication and reconciliation between multiple systems and platforms
• Reverse engineer existing calculations and workflows to identify the root causes of mismatches or defects
• Investigate and resolve MTM (mark-to-market) mismatches through detailed quantitative and data analyses
• Raise an alert and track and support the resolution of production- or calculation-related defects in collaboration with development teams
• Participate in User Acceptance Testing (UAT) and regression testing activities to validate system changes and enhancements
• Develop and maintain Excel models, VBA automation tools and Python-based analytical solutions to improve efficiency and accuracy
• Analyse large datasets and validate data to ensure consistency and reliability across systems
• Document findings, reconciliation methodologies, testing results and process improvements clearly and accurately
• Work closely with stakeholders, developers and quantitative teams to support issue resolution and process optimisation