Equity & fixed income fund analyst

Purpose of the role

Analyse, monitor, and conduct due diligence on long-only equity managers, fixed income/sukuk managers, and REIT/infrastructure funds in the global portfolio. Cover Layer 1 (index-replicating SMAs) and Layer 2 (multi-factor, style-based) managers. Work directly with the AI Lab’s tools to amplify analytical capacity and contribute to the institutional knowledge base.

Key responsibilities

• Monitor and analyse 10–15 equity and fixed income fund managers in the global portfolio: performance attribution, factor exposure analysis, strategy adherence, and peer comparison.
• Build and maintain manager review packages for IC quarterly reviews, incorporating AI Lab factor decomposition and anomaly detection outputs.
• Conduct due diligence on prospective equity and FI managers sourced from eVestment/Mercer screening: DDQ analysis, reference checks, on-site visit preparation.
• Monitor Layer 1 SMA managers (e.g., BlackRock global equity SMA, sukuk SMA): track benchmark deviation, rebalancing activity, and fee verification.
• Analyse Layer 2 factor-based strategies: validate factor loadings against stated mandate, identify unintended factor tilts, assess factor timing decisions.
• Contribute to the SAA review process: provide asset class outlook inputs for equity and fixed income allocations.
• Maintain the fund manager knowledge base: ensure all manager communications, quarterly letters, and DDQ responses are ingested into the Document Intelligence Engine.

Qualifications and experience

• CFA Level II+ or equivalent. 3–5 years of buy-side experience in equity and/or fixed income fund analysis.
• Strong analytical skills: financial modelling, performance attribution, risk analysis.
• Proficiency in Excel, Bloomberg/Capital IQ, and willingness to adopt AI Lab tools (dashboards, chat interface, screening algorithms).
• Excellent written communication: ability to produce IC-quality memoranda with clear recommendations.
• Bachelor’s or Master’s degree in finance, economics, or quantitative discipline.
• Self-starter comfortable working independently in a distributed team environment.