Quant Research - Quant Associate Programme - 2027

Start Date: Flexible between January and September 2027

Location: London/ New York (note we have significantly more opportunities available in London)

About the programme

You will be working in a position designed for high-calibre, highly numerate individuals within our quantitative teams. We value free thinkers, and we have created a fast-paced and meritocratic environment where you will be encouraged to apply your own initiative and challenge conventional wisdom. You will be delivering on the research agenda, in addition to back testing/researching forecasts of asset returns on horizons of hours to years. We all share a tireless drive for innovation and participants tend to be one of the top students at their university.

Previous associates are now involved in creating and optimising our signature systematic trading models. As a member of our Quantitative Associate Programme you will be part of a select cohort receiving on the job training from experienced colleagues who have a matchless record of helping high performing individuals reach their true potential. You will research, develop, and refine the predictive signals and models that drive our investment strategies — turning data into alpha. The work spans statistical modelling, machine learning, large-scale data analysis, and signal research: sourcing and exploring novel datasets, designing and validating trading signals, rigorously backtesting hypotheses against real market conditions, and collaborating with portfolio managers to bring the strongest ideas into production. This is an intellectually demanding role where you will push the boundaries of what our models can capture and continually sharpen our competitive advantage.

What we look for:

• Master's degree or PhD in a highly quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, Engineering, or a related field
• Strong programming skills in Python, C++, or similar languages
• A solid foundation in statistics, probability, and numerical methods
• Genuine interest in financial markets and systematic trading
• Sharp problem-solving ability and a rigorous, analytical mindset
• The ability to work under pressure and manage competing priorities in a fast-paced, live trading environment
• Clear communication skills - you can explain complex technical concepts to a range of audiences
• A proactive, self-starter attitude with the drive to take ownership of projects from day one

Marshall Wace is an equal opportunity employer. Individuals seeking employment are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, sexual orientation, or any other category protected by applicable law.

In accordance with New York State and New York City pay transparency laws, the annual base salary for this position is $150,000 - $165,000. Actual compensation may vary based on factors such as experience, qualifications, and location. This role may also be eligible for a discretionary bonus and benefits.

You must maintain the strict confidentiality of all information disclosed to you or made available to you during any stage of the recruitment process. This includes, but is not limited to, interview questions, test questions, assessments, presentations, and any other proprietary, confidential, or commercially sensitive information relating to Marshall Wace, its employees, business operations, or recruitment procedures.

Any breach of these confidentiality obligations will result in the immediate termination of your candidacy for any position at Marshall Wace. In addition, Marshall Wace reserves the right to pursue any other remedies available at law or in equity, including but not limited to legal action for damages or injunctive relief.

Your strict adherence to these confidentiality requirements is essential to protect the integrity and fairness of our recruitment process. By proceeding with your application, you confirm your understanding of, and agreement to, these confidentiality obligations, as well as your acceptance of our privacy policy.

Thank you for your cooperation and professionalism.