Quantitative Researcher - Systematic Team | UAE

A leading investment team in The UAE is seeking a highly skilled Quantitative Researcher to play a key role in advancing systematic research and decision-making systems. This position blends traditional portfolio research with cutting-edge AI applications, offering the opportunity to shape and deliver production-grade solutions that directly impact investment evaluation and analytics.

The successful candidate will act as the team’s practical AI lead, owning the knowledge graph, GNN/graph-embedding models, and LLM/NLP extraction + RAG systems that power investment workflows. Alongside this, the role will involve designing research processes, monitoring portfolios, and contributing to systematic fund-of-funds strategies.

Key Responsibilities

• Systematic Research & Investment Support:
• Conduct research to design and enhance systems that identify and evaluate opportunities across external managers and systematic strategies relevant to fund-of-funds portfolios.
• Apply quantitative and fundamental techniques to assess performance drivers, risk decomposition, factor/style exposures, and persistence.
• Develop and maintain models for investment decision-making, including manager screening, portfolio construction, monitoring, and scenario/sensitivity analysis.
• Monitor portfolios of hedge funds and traditional funds, designing and recommending overlay strategies or hedges where appropriate.
• Produce detailed reports and presentations for senior stakeholders, synthesizing manager interviews into clear, well-documented insights.
• AI & Data Systems:
• Design and maintain domain ontologies, building and operating knowledge graphs (e.g., Neo4j) with versioning, provenance, consent/visibility controls, and schema evolution.
• Build NLP/LLM pipelines for information extraction across diverse document sources; develop hybrid retrieval systems with evaluators for relevance, faithfulness, and citation.
• Train embeddings for nodes and relations, prototype GNNs and advanced hypergraph/VGAE models, and run prediction tasks to enrich the knowledge graph.
• Collaborate with researchers, portfolio managers, and technology teams to ensure solutions are integrated, scalable, and optimized for investment workflows.
• Create and maintain comprehensive documentation, providing knowledge transfer and training to ensure best practices across teams

Preferred Qualifications:

• Experience in financial services (e.g., brokerage, asset management, or banking) or a strong macroeconomic research background
• Familiarity with machine learning, NLP, and large language models (LLMs)
• Knowledge of various datasets (e.g., earnings, filings, credit card, CCTV)
• Master’s degree in a relevant field is a plus