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

iSAM Securities regulated by the FCA, SFC, and CIMA registered, is a leading algorithmic trading firm and trusted electronic market maker, providing liquidity, technology and prime services to institutional clients and trading venues globally. The firm offers full-service prime brokerage and execution via its cutting-edge 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 is unique, provides a specialist quantitative approach and is designed to deliver highly diversifying absolute returns for institutional portfolios.

The role

We are seeking a highly motivated Quantitative Researcher to join iSAM Vector, a systematic fund serving institutional investors. You will contribute to the research, development and monitoring of systematic trading strategies across global markets. Working closely with researchers, technologists and execution specialists, you will help generate and test new investment ideas, improve existing strategies, and support the full research lifecycle — from hypothesis generation and data analysis through to backtesting, implementation and live strategy monitoring. This is an opportunity for an early-career researcher to gain hands-on experience in a collaborative, research-driven investment environment, contributing directly to the continued development of a large systematic fund. You will join a systematic fund serving institutional investors, with exposure to the full strategy lifecycle from research through to live trading. The role offers the opportunity to work closely with experienced researchers and technologists, contribute to production investment strategies, and develop practical expertise in systematic trading within a rigorous, collaborative research environment.

Responsibililtes

• Developing new signals and research ideas across global markets, from initial research questions through to testing, validation and implementation
• Enhancing existing systematic strategies through signal refinement, model improvements and rigorous empirical testing
• Analysing financial market data to identify, test and validate new investment ideas
• Researching and backtesting systematic signals across markets, instruments and time horizons
• Supporting the development of portfolio construction, risk management and implementation techniques
• Building an understanding of how research ideas are translated into live trading strategies, from signal design through to implementation and monitoring
• Working with researchers and technologists to translate research ideas into robust production-ready trading signals
• Developing research tools, datasets and analytical infrastructure to improve the research process
• Communicating research findings clearly to technical and non-technical stakeholders

Qualifications

• A strong academic background in a quantitative discipline such as mathematics, statistics, physics, engineering, computer science, economics or finance
• Strong programming skills, preferably in Python, with experience using data analysis libraries such as Pandas and NumPy
• A solid understanding of statistics, probability, time-series analysis, optimisation or machine learning
• Interest in financial markets, systematic investing and empirical research
• Ability to work with large, complex datasets and draw robust conclusions from noisy data
• Ability to work through a full research pipeline, including data analysis, hypothesis testing, signal construction, robustness checking and backtesting
• A rigorous approach to research design, backtesting and model validation

Useful but not essential:

• Prior experience in quantitative research, systematic trading, asset management or a research-focused data science role
• Familiarity with portfolio construction, risk management, transaction cost analysis or signal research
• Experience with futures, FX, equities, rates, commodities or other liquid markets
• Exposure to machine learning, econometrics or alternative datasets

 Personal Attributes

• Strong communication skills and the ability to explain technical ideas clearly
• Curiosity, intellectual honesty and a willingness to challenge assumptions