Quantitative Research Intern

We are looking for academically strong and motivated undergraduate students to participate in our Summer 2027 Quant Research Internship program. You will be working with large financial datasets to uncover valuable insights feeding in our end-to-end machine learning pipeline for equities. You will be joining a small but long-established team with offices in New York and London for a 10 week program focused on a structured data-driven research project, delivering a finished product with measurable impact within that time frame. While you work on your project, you will be expected to show broader interest in the work of your co-participants in the program, and collaborate in research discussions. You will have access to a wide range of resources including access to our proprietary on-prem computational cluster and broad data library, as well as the latest LLM tools. We will be also providing mentorship and regular feedback on your progress. Selection process • Resume screening. [from mid-September] • Short intro call [optional] • Take-home task focused on machine learning and LLM tools. [from Oct 1st, on a rolling basis] • Interview (1.5 hours) mostly focused on the take-home task and resume. [in person, from Dec 1st, on a rolling basis]

Requirements • Pursuing an undergraduate degree in quantitative / STEM field from a top UK university (candidates currently studying for a graduate degree will be considered) • Expected to complete your undergraduate degree in 2027 (including candidates intending to continue directly into a master’s degree) • Familiar with probabilistic and statistical reasoning • Comfortable with python ecosystem when dealing with data manipulation and regression tasks • Curious about the financial domain • Have an engineering mindset and a builder's mentality

Benefits • Annual compensation of £100,000 (pro-rated for the duration of the internship) • Arranged housing • Canteen allowance (Deliveroo) • Company-organised cultural events and outings throughout the Summer