Quantitative Analyst | Quantitative Research Analyst

Role Overview

We are seeking a detail-oriented quantitative professional to drive financial research and quantitative analysis efforts. This part-time onsite role focuses on utilizing mathematical modelling and statistical techniques to extract insights from large financial datasets and contribute to strategy formulation supporting investment and corporate decisions.

Key Responsibilities

• Gather, preprocess, and analyze extensive datasets to uncover trends and meaningful signals.
• Build and validate statistical and mathematical models to support rigorous quantitative research.
• Design and conduct hypothesis testing, historical analyses, and backtesting of models.
• Evaluate, monitor, and improve the performance and reliability of quantitative models.
• Collaborate effectively with researchers, investment specialists, developers, and other parties to enhance analytical methodologies and deliver solutions.
• Document findings from quantitative research and clearly communicate complex results to stakeholders.
• Assess limitations and risks associated with models and data-driven strategies.

Required Qualifications and Experience

• In-depth knowledge of quantitative analysis, financial markets, statistics, and mathematical modelling.
• Proficiency in Python, R, MATLAB, or comparable quantitative programming languages.
• Sound understanding of topics including probability theory, statistics, linear algebra, calculus, and numerical methods.
• Experience handling large, complex datasets and deriving actionable insights.
• Familiarity with financial instruments, investment principles, market dynamics, and portfolio management concepts.
• Expertise with statistical modelling, forecasting methods, hypothesis testing, and quantitative research practices.
• Preferred experience in backtesting, factor analysis, signal detection, and validation techniques.
• Advantageous skills include SQL proficiency and knowledge of database/data processing tools.
• Beneficial familiarity with machine learning algorithms, time-series analytics, optimization strategies, or econometrics.
• Strong analytical reasoning, critical thinking, and problem-solving capabilities.
• Ability to effectively communicate sophisticated quantitative ideas and outcomes with precision.
• High level of attention to detail ensuring data quality, model validity, and accurate analysis.
• A curious and rigorous research-driven attitude, committed to continuous learning and innovation in quantitative finance methods.