Quantitative Analyst; Fully Remote
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. #J-18808-Ljbffr
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