Quantitative Analyst | Quantitative Research Analyst

Role DescriptionWe are looking for a highly analytical and quantitative professional to support quantitative analysis, financial research, statistical modelling, and data-driven decision-making. The role involves developing analytical models, researching financial and market data, identifying patterns and opportunities, and translating quantitative findings into actionable insights.Key responsibilities include collecting, cleaning, and analyzing large datasets, developing statistical and mathematical models, conducting quantitative research, and evaluating market trends and investment signals. The role will involve testing hypotheses, performing historical analysis and backtesting, assessing model performance, and supporting the development and refinement of quantitative strategies.The successful candidate will work with researchers, investment professionals, developers, and other stakeholders to develop analytical solutions and improve research methodologies. The role also involves documenting research findings, monitoring model performance, identifying potential risks and limitations, and applying rigorous quantitative methods to support investment and business decisions.QualificationsStrong understanding of quantitative analysis, statistics, financial markets, mathematical modelling, and data analysis.Strong proficiency in Python, R, MATLAB, or other quantitative programming languages.Solid knowledge of statistics, probability, linear algebra, calculus, and numerical methods.Ability to work with large and complex datasets and extract meaningful insights.Familiarity with financial instruments, investment concepts, market behaviour, and portfolio analysis.Knowledge of quantitative research techniques, statistical modelling, forecasting, and hypothesis testing.Understanding of backtesting, factor analysis, signal generation, and model validation is preferred.Strong proficiency in SQL and experience with databases or data-processing tools is an advantage.Familiarity with machine learning, time-series analysis, or optimization techniques is beneficial.Strong analytical, logical, critical-thinking, and problem-solving skills.Ability to communicate complex quantitative concepts and research findings clearly.Strong attention to detail and commitment to data quality, model integrity, and analytical accuracy.Curious, rigorous, and research-oriented mindset with a strong interest in quantitative methods, financial data, and continuous innovation.