PhD Quantitative Research Internship

Role: PhD Quantitative Research Internship

Base Office: London

About the Role:

iSAM is offering Quantitative Research Internship opportunities for PhD students in their penultimate year of study in a quantitative discipline. These internships will take place during Winter 2026 and Summer 2027 and will run for 12 weeks.

Roles are available across three key areas of the business:

• Quantitative Trading within iSAM Securities
• Quantitative Research within the iSAM Options desk
• Quantitative Research within iSAM Funds

As an intern, you will be fully embedded within your team and contribute meaningfully to live research and trading initiatives. The role is research-focused and involves applying advanced statistical and mathematical techniques to develop and evaluate quantitative signals and strategies.

Responsibilities:

You will work as part of a collaborative research team, tackling complex and intellectually challenging problems. Responsibilities may include:

• Assisting in the research and development of systematic investment strategies across multiple asset classes
• Analysing large and complex financial datasets to identify signals, patterns, and risk characteristics
• Designing, implementing, and testing quantitative models using Python and relevant numerical and statistical libraries
• Supporting the backtesting, performance analysis, and validation of trading strategies
• Helping to maintain and enhance research infrastructure, tools, and data pipelines
• Clearly documenting research methodologies and results, and presenting findings to senior researchers
• Collaborating closely with portfolio managers, quantitative researchers, and technologists
• Investigating enhancements to existing strategies, including improvements to risk management and execution assumptions

Qualifications

• PhD student in a quantitative field (e.g. Mathematics, Physics, Statistics, Computer Science), with expected completion in 2026 or 2027
• Strong foundation in statistics and probability theory, with familiarity with machine learning techniques
• Strong programming skills in Python (experience with libraries such as NumPy, Pandas, or similar is desirable)
• Experience working in a research-driven environment, including handling large datasets and developing algorithmic solutions to complex problems
• A strong interest in financial markets and systematic trading (prior finance experience is not required)

Personal Attributes

• Highly analytical, with a strong sense of ownership and accountability
• Enjoys tackling complex problems and working through challenging mathematical or statistical questions
• Collaborative and able to work effectively with researchers, technologists, and trading teams
• Clear and concise communicator, both verbally and in writing
• Comfortable working independently while knowing when to seek input from others

Key Objectives

By the end of the internship, a successful candidate will have:

• Developed a strong understanding of how quantitative research is conducted within a live trading environment
• Contributed tangible research outputs that inform or enhance existing trading strategies or research directions
• Demonstrated the ability to translate complex mathematical and statistical ideas into robust, well-tested code
• Gained hands-on experience working with large-scale financial data and research infrastructure
• Built an understanding of the full research lifecycle, from idea generation and data analysis through to validation and presentation
• Established effective working relationships within their team, contributing proactively and collaboratively to shared objectives
• Strengthened problem-solving, communication, and technical skills in a fast-paced, intellectually rigorous setting