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- Tasks: Drive innovative research and AI solutions to enhance investment strategies and decision-making.
- Company: Leading investment team in the UAE with a focus on cutting-edge technology.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Why this job: Shape the future of investment with AI and make a real impact in finance.
- Qualifications: Experience in finance or macroeconomic research; familiarity with machine learning and NLP is a plus.
- Other info: Collaborative team culture with a focus on innovation and career development.
The predicted salary is between 36000 - 60000 £ per year.
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
Quantitative Researcher - Systematic Team | UAE employer: Durlston Partners
Contact Detail:
Durlston Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher - Systematic Team | UAE
✨Tip Number 1
Network like a pro! Reach out to professionals in the investment and AI sectors on LinkedIn. Join relevant groups and participate in discussions to get your name out there and show off your expertise.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in quantitative research and AI applications. Practice common interview questions and be ready to discuss your past projects and how they relate to the role.
✨Tip Number 3
Showcase your work! Create a portfolio that highlights your quantitative models, research papers, or any relevant projects. This will give potential employers a tangible sense of your capabilities and how you can contribute to their team.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Quantitative Researcher - Systematic Team | UAE
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in quantitative research and AI applications. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about systematic research and how you can contribute to our team. Keep it engaging and make sure to connect your background to the job description.
Showcase Your Technical Skills: Since this role involves advanced AI and data systems, be sure to highlight your technical skills clearly. Mention any experience with knowledge graphs, NLP, or machine learning models that you've worked on – we love seeing practical examples!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it’s super easy!
How to prepare for a job interview at Durlston Partners
✨Know Your Numbers
As a Quantitative Researcher, you'll need to demonstrate your analytical skills. Brush up on key financial metrics and be ready to discuss how you've used quantitative techniques in past roles. Prepare examples that showcase your ability to assess performance drivers and risk decomposition.
✨Showcase Your AI Savvy
Since this role involves cutting-edge AI applications, make sure you can talk about your experience with machine learning, NLP, and knowledge graphs. Be prepared to discuss specific projects where you've implemented these technologies and the impact they had on investment workflows.
✨Prepare for Technical Questions
Expect technical questions related to GNNs, graph-embedding models, and LLM/NLP extraction. Review relevant concepts and be ready to explain them clearly. Practising coding problems or case studies related to portfolio construction and monitoring can also give you an edge.
✨Communicate Clearly
You'll need to produce detailed reports and presentations for senior stakeholders, so practice summarising complex information into clear insights. Think about how you can convey your findings from manager interviews or research processes in a concise and engaging way.