Quant Data Scientist (LLMs) - UAE Relocation

JR United Kingdom; Chester; On-site; GBP 40,000 - 60,000; Full time

Job summary

A leading company in the finance sector is seeking a Quant Data Scientist to join a high-caliber team. This role will focus on leveraging data and language models to support systematic investment strategies. The successful candidate will work on building data pipelines, ensuring the quality of LLM outputs, and collaborating with researchers to enhance model performance. A strong grounding in quantitative disciplines and 3+ years in data science is essential.

Qualifications

• 3+ years of professional experience in Data Science or Quantitative roles within finance.
• Familiarity with financial data sources and data modelling techniques.
• Experience with LLM fine-tuning or NLP methodologies preferred.

Responsibilities

• Develop and maintain automated data pipelines.
• Enhance quality control processes for financial content generated by LLMs.
• Collaborate with quant researchers to improve AI-driven models.

Skills

Proficiency in Python

Proficiency in SQL

Data processing libraries

Problem-solving skills

Machine learning frameworks

Education

Bachelor's or Master’s degree in a quantitative discipline

Tools

pandas

numpy

Job description

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Quant Data Scientist (LLMs) - UAE Relocation, chester

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Client:

Durlston Partners

Location:

chester, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Views:

4

Posted:

04.06.2025

Expiry Date:

19.07.2025

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Job Description:

Quant Data Scientist - Hedge Fund - UAE Relocation - Leading Comp

We are seeking a Data Scientist to join a high-calibre Quant team within a discreet fund based in the UAE, operating at the intersection of Quantitative Research and Artificial Intelligence. This role is dedicated to leveraging data and language models to support the research, development, and implementation of systematic investment strategies. The successful candidate will combine strong technical skills with a solid understanding of financial markets and a passion for innovative data applications.

Key Responsibilities

• Develop and maintain automated data pipelines for ingestion, tagging, cleansing, and integration of financial text and structured data.
• Oversee and enhance quality control processes for financial content generated by large language models (LLMs), ensuring coherence, accuracy, and logical consistency.
• Collaborate with quant researchers and engineers to improve the performance, accuracy, and reliability of AI-driven models.
• Conduct descriptive and exploratory analysis to uncover patterns, trends, and potential applications across a wide range of datasets.
• Maintain robust and scalable data infra to support real-time and batch processing workflows.
• Provide domain expertise to help shape financial knowledge representation in LLM outputs.
• Develop and apply custom quality metrics to assess and guide model performance.
• Actively participate in development cycles, research discussions, and cross-functional knowledge-sharing sessions.

Required Qualifications

• Bachelor's or Master’s degree in a quantitative discipline (e.g., Computer Science, Finance, Economics, Statistics, Engineering).
• 3+ years of professional experience in Data Science or Quantitative roles within finance.
• Proficiency in Python and SQL with exp. in data processing libraries such as pandas/numpy.
• Experience with machine learning frameworks and statistical analysis tools.
• Familiarity with financial data sources, APIs, and data modelling techniques.
• Ability to design, implement, and maintain scalable data pipelines and workflows.
• Excellent problem-solving skills and the ability to communicate complex ideas clearly to both technical and non-technical audiences.

Preferred Qualifications

• Experience with LLM fine-tuning, prompt engineering, or other NLP methodologies.
• Strong curiosity and motivation to explore new technologies and methodologies in data science and AI
• Strong understanding of macroeconomics, financial markets, and investment principles.