Systematic strategies start with a question
The Quantitative Research teams are at the core of Marshall Wace’s systematic investment process. As a quantitative researcher, you’ll develop, test, and refine the signals and models that drive portfolio performance — working with experienced researchers on live alpha research from day one.
Your work will have a direct impact on the business-critical functions of the firm. You’ll take ownership of research projects, design experiments, and see your ideas through from hypothesis to production strategy. This is research with consequences — the models you build inform real capital allocation decisions at one of Europe’s largest systematic hedge funds.
This is not a role where you’ll sit on the sidelines. From your first week, you’ll be embedded in one of our Systematic Investment teams, contributing to the research agenda across multiple timeframes, geographies, and asset classes.
What you’ll work on
Alpha research and signal generation — identify and develop predictive signals across equities, futures and other asset classes, using statistical and machine learning techniques
Backtesting and strategy validation — rigorously test hypotheses across multiple timeframes, geographies, and market conditions
Forecasting asset returns — build models that forecast returns from intraday to multi-year horizons
Portfolio construction research — contribute to how signals are combined, weighted, and translated into positions
Cross-team collaboration — work alongside quant implementation engineers, portfolio managers, and data scientists in a shared codebase environment
Example projects
Developing a new cross-sectional signal using alternative data and validating it across global equity markets
Researching regime-detection models to adapt strategy behaviour in different market environments
Analysing execution cost models to improve the net-of-cost performance of systematic strategies
Building and evaluating machine learning pipelines for return prediction across multiple asset classes
What we’re looking for
Academic background
Excellent academic record from a top-tier university. Minimum of a Master’s degree in a quantitative discipline — mathematics, statistics, physics, machine learning, computer science, econometrics, or similar.
Experience
We welcome recent graduates and early-career researchers with up to 3 years of relevant experience. Prior industry experience is valued but not required — strong academic research or quantitative project work is equally compelling.
Technical profile
You have strong statistical and mathematical foundations — probability, statistics, linear algebra, time-series analysis, and machine learning. You have strong coding skills, with experience working with data and modelling libraries at scale. You approach research with rigour — you design experiments, question results, and iterate. You can communicate complex quantitative ideas clearly.
Mindset
You question assumptions and follow the evidence, wherever it leads
You’re intellectually curious beyond the immediate task at hand
You balance rigour with pragmatism — research that doesn’t reach production has limited value
You collaborate openly, sharing ideas and building on others’ work
Programme structure
Location: London
Team: Quantitative Research, within Systematic Investment
Development: regular internal talks, mentoring, structured personal development programme
Environment: collaborative, shared codebase, meritocratic — your ideas compete on their merits, not your seniority