Quantitative Analyst; Fully Remote Job Singapore,Finance & Banking

Position: Quantitative Analyst (Fully Remote)

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 counter party 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 (stats models, scikit-learn).
Experience with databases (SQL or No

SQL) 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.

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