Quantitative Research Analyst

Role Overview

We are looking for an analytically skilled Quantitative Research Analyst to perform advanced quantitative research, build statistical models, and derive insights to support strategic, financial, and business decisions. This position involves developing research methodologies, analyzing large datasets, constructing predictive models, and presenting research outcomes that inform actionable strategies. The role requires expertise in mathematics, statistics, data science, finance, and programming to address diverse analytical challenges across business domains.

Key Duties

• Design and execute quantitative research using statistical and mathematical techniques.
• Gather, clean, validate, and analyze structured and unstructured data from various internal and external sources.
• Develop predictive, forecasting, risk, and optimization models to aid investment and business decisions.
• Apply time series, regression, hypothesis testing, simulation, and machine learning methods to identify trends and opportunities.
• Conduct market, financial, economic, and industry analyses to back strategic projects.
• Validate and review research approaches ensuring accuracy and robustness of models.
• Perform backtesting, scenario and sensitivity analyses, and monitor model performance.
• Collaborate across teams including data scientists, software engineers, investment professionals, and stakeholders to translate findings into practical solutions.
• Prepare technical documentation, research papers, visual dashboards, and presentations to explain complex analytics.
• Support data governance and maintain research best practices and documentation.
• Identify automation opportunities to enhance research efficiency.
• Track emerging trends in quantitative finance, AI, machine learning, and analytics.
• Contribute to the ongoing improvement of quantitative research methodologies and frameworks.

Qualifications

• Bachelor's degree in disciplines like Mathematics, Statistics, Quantitative Finance, Financial Engineering, Economics, Data Science, Computer Science, Physics, Engineering, or Operations Research.
• Strong knowledge of probability theory, statistics, econometrics, optimization, numerical methods, linear algebra, and calculus.
• Proficiency in programming languages such as Python, R, MATLAB, Julia, or C++.
• Experience with statistical analysis, predictive modeling, machine learning, data mining, and research methods.
• Understanding of financial markets, portfolio theory, derivatives, risk management, or asset pricing is a plus.
• Skilled in SQL and managing relational and non-relational databases.
• Familiarity with data visualization and BI tools like Power BI or Tableau.
• Knowledge of cloud, distributed, or high-performance computing environments is advantageous.
• Exceptional analytical and quantitative problem-solving capabilities with attention to complex datasets.
• Strong communication skills for preparing reports, presentations, and technical documentation.
• Detail-oriented with high integrity and accuracy in work.
• Ability to work autonomously and within cross-disciplinary teams.
• Experience with version control systems such as Git and software development practices is a benefit.
• Certifications like CFA, FRM, CQF, PRM, or credentials in data science, machine learning, or quantitative finance are valued but not required.
• Dedication to continuous learning in quantitative research, AI, machine learning, fintech, statistical modeling, and data analytics.