Quantitative Analyst (Fully Remote)

Position: Quantitative Analyst

Compensation: USD $130,000 per annum + up to 15% Performance Bonus

Total Annual Package: Up to USD $149,500

Location: Remote

We are seeking a Quantitative Analyst to join our data-driven research team focused on leveraging alternative data and sentiment analysis for market insights. This role emphasizes in-depth quantitative research, model development, and rigorous backtesting of signals to drive actionable strategies. The ideal candidate will have a passion for financial markets and expertise in transforming raw data into clear, data-informed insights.

This position is remote, with the option to work from our Dubai office (with 0% income tax), if preferred (relocation and visa sponsorship support available).

Key Responsibilities:

Hedge Funds:

• Conduct comprehensive quantitative analysis of hedge fund returns, risk metrics, and factor exposures to evaluate manager skill and strategy persistence
• Develop and maintain proprietary analytical frameworks to decompose hedge fund performance, identify style drift, and assess risk-adjusted returns across market cycles
• Perform detailed attribution analysis to validate managers' stated investment processes and verify alignment with reported results
• Build and maintain risk factor models to evaluate strategy correlations, beta exposures, and potential portfolio overlaps across our manager universe
• Analyze portfolio-level characteristics including liquidity profiles, position-level concentration, and counterparty exposures
• Provide quantitative support to the CIO for manager evaluation and ongoing monitoring
• Create detailed analytical reports for the investment committee, synthesizing complex quantitative findings into actionable insights

Other Asset Classes:

• Acquire, clean, and normalize various alternative datasets (e.g., sentiment, social media, and ESG sources)
• Develop and refine predictive models and signals using time-series analysis, statistical modeling, and machine learning
• Create robust backtesting frameworks to evaluate model performance and incorporate transaction cost or market impact
• Build and monitor risk models, conduct stress testing under different market scenarios
• Document and present research findings, methodologies, and performance metrics to stakeholders

Required Qualifications

• Master's degree in Finance, Economics, Mathematics, Computer Science, Engineering, Financial Engineering, Statistics, or a related quantitative field (required)
• 3+ years of experience in quantitative research, data science, or analytics within a leading financial institution (e.g., top-tier investment bank, asset manager, hedge fund, or proprietary trading firm)
• Proven track record of building and validating quantitative models in real-world market environments.
• Proficiency in Python for data analysis (pandas, numpy, scipy) and modeling (statsmodels, scikit-learn).
• Experience with databases (SQL or NoSQL) and large-scale data processing frameworks.
• Familiarity with statistical techniques (time-series analysis, regression, factor modeling, signal processing).
• Solid understanding of financial market structure, pricing, and liquidity.
• Knowledge of key asset classes (equities, fixed income, or derivatives).
• Candidates must have completed all academic programs; those currently enrolled in part-time or full-time degree programs (e.g., part-time Master's, MPhil, PhD coursework) are not eligible

Preferred Qualifications

• PhD in a quantitative field (Financial Engineering, Statistics, or similar).
• Experience analyzing sentiment or alternative data (news feeds, social media, ESG, etc.).
• Background in machine learning, deep learning, or NLP for financial forecasting.
• Familiarity with cloud computing environments (AWS, GCP, or Azure) for large-scale data processing.
• Experience with portfolio optimization, risk analytics, or factor investing.