Math Finance Summer Internship (Masters/PhD)

Math Finance Summer Internship

Programme starts June 2025

Duration: 12 Weeks

Location: New York, New York

Our Math Finance Summer Internship Programme has been crafted so that you can understand what a career on the Strats and Modelling team at LSEG would be like. Our internships are actual roles within LSEG. As an intern, you play a part in the success of our organisation.

To be eligible, you must be in your penultimate year of graduate study and due to complete your degree on or before summer 2026. You should have a predicted PhD or master’s degree in Math, Physics, Financial Engineering or other quantitative fields.

This position is within the Strats and Modelling Team within the Post Trade Engineering division and will be based in New York. 

Internship Summary

The Strats & Modelling team is responsible for designing, building and maintaining the algorithms at the heart our services. This typically involves creating a model for a linear of convex optimisation problem and interpreting the solution of that problem as a set of financial transactions that should be executed to improve some aspect of a derivatives portfolio. While we use a commercial optimization library for the optimisation itself, the scale of the problems that we encounter mean that we are on the leading edge of what today’s software can handle and so we need to have a deep understanding of the behaviour of the algorithms.

The successful candidate will work as part of a small team of 5-7 strats to build and research improvements to the Compression engine. They will work directly with the Product Development team to enhance the product, based on feedback from clients and analysis of runs, as well as on strategic projects. Examples of possible projects include:

• Develop new scaling scheme to improve reliability and performance
• Use the solution of our current MIP optimiser as a starting point to a non-linear solver. This would allow greater flexibility in the solutions we propose.
• Investigate how sensitive the solution is to small changes in the data to understand which constraints we should relax for maximum impact
• Introduce new variables in the optimiser to allow variable hedge rates in our proposals
• Improve the runtime performance by investigating and adding heuristics to reduce the data set and solution search space
• Develop new functionality to better validate incoming risk data prior to optimisation.

Desired skills:

• Solid understanding of python for numerical programs. Familiarity with pandas and numpy
• A strong mathematical background (numerical methods, linear algebra, probability & statistics)
• Understanding of linear programming, mixed integer programming and convex optimisation
• Knowledge of Interest Rate Swaps
• Excellent problem-solving skills

Applications Close: 27 November. However, we recommend an early application because we consider candidates on a rolling basis. Depending on the volume of applications, we may close the application process earlier.

• The first stage of the recruitment process involves a review of your CV/application.

• Should you pass the initial screening stage, we will invite you to complete an online skills assessment.

• The next step is a virtual interview where we will gain a deeper understanding of your motivations for the role and assess your understanding of some of our key competencies.

• The final stage is an assessment centre which involves several different types of exercises - further information is provided at the point of invite.