Quant Research - Quant Associate Programme - 2027
This associate programme develops signals and predictive models for systematic investing. Research spans statistical methods, machine learning and empirical evaluation before deployment with portfolio managers.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Explore datasets for economically plausible predictors
- Build and validate candidate return-forecasting signals
- Evaluate hypotheses through realistic historical simulations
- Present robust research for potential portfolio deployment
What they ask for
Required
- Postgraduate quantitative training
- Python, C++ or comparable coding skills
- Probability and statistics
- Numerical techniques
- Analytical problem-solving
- Clear technical communication
- Managing competing priorities
- Education
- Master or doctoral qualification in a strongly quantitative subject.
- Experience
- Early-career associate intake; start between January and September 2027.
- Languages and tools
How to prepare
- Build a reproducible return-prediction experiment
- Compare statistical and machine-learning baselines out of sample
- Explain signal decay, data timing and realistic execution costs
What to study
- Probability, Statistics and Financial Data
- Quantitative Research, Data and Backtesting
- Financial Machine Learning
The Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Marshall Wace’s site before applying: requirements, location and pay can change after we check them.
View the posting on Marshall Wace’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Marshall Wace and doesn’t handle applications.