PhD Quantitative Research Internship

About iSAM

iSAM is an innovative financial technology firm specialising in quantitative trading, comprising iSAM Funds and iSAM Securities.

iSAM Securities, regulated by the FCA and SFC and registered with CIMA, is a leading algorithmic trading firm and trusted electronic market maker. It provides liquidity, technology and prime services to institutional clients and trading venues globally. The firm offers full-service prime brokerage and execution through its proprietary technology, as well as market-leading analytics cleared through the group’s bank prime brokers.

iSAM Funds is an alternative asset manager specialising in systematic investing. Each strategy provides a specialist quantitative approach and is designed to deliver highly diversifying absolute returns for institutional portfolios.

Role Details

• Role: PhD Quantitative Research Internship
• Base office: London
• Duration: 12 weeks
• Internship periods: Winter 2026 and Summer 2027

About the Role

iSAM is offering quantitative research internship opportunities for PhD students in their penultimate year of study in a quantitative discipline.

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, such as Mathematics, Physics, Statistics or 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 and Pandas, or similar, is desirable
• Experience working in a research-driven environment, including handling large datasets and developing algorithmic solutions to complex problems
• 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