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BarclaysHong KongInternship

Quantitative Analytics Associate Off Cycle Internship 2027 Hong Kong

This 6-month Quantitative Analytics internship in Hong Kong offers postgraduate students hands-on experience in developing quantitative models and trading strategies, collaborating with business partners, and enhancing risk management systems from June to December 2027.

1

Role and Responsibilities

  • Develop and implement quantitative models to improve decision-making, pricing, and risk management.
  • Design and maintain high-performance trading platforms and risk systems with new features.
  • Conduct research and data analysis to identify market trends and drive innovation.
  • Collaborate with traders, sales, risk, and finance teams to deliver tailored solutions.
  • Work cross-functionally with compliance, IT, and strategy teams to improve trading infrastructure.
2

Team and Environment

  • Join a global Quantitative Analytics team specializing in valuation and risk management models.
  • Work closely with traders and stakeholders across the bank to develop models and tools.
  • Gain exposure to various modelling techniques and asset classes.
  • Benefit from a collaborative and supportive environment with expert colleagues.
  • Build relationships with senior leaders and peers during the programme.
3

Candidate Requirements

  • Postgraduate student graduating between December 2027 and June 2028.
  • Degree in Physics, Mathematics, Operations Research, Quantitative Finance, Economics, Statistics, Stochastic Calculus, Computer Science, or related STEM fields.
  • Strong programming skills in Python, C++, or Java.
  • GPA of 3.2 or above preferred.
  • Excellent communication and collaboration skills.
4

Programme Details

  • Duration: 6 months, June to December 2027, based in Hong Kong.
  • Comprehensive training provided throughout the internship.
  • Opportunity for full-time employment upon successful completion.
  • Focus on solving real-world quantitative and technology problems.
  • Work on improving computing and data infrastructure.