An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Synthesis is seeking academically strong undergraduate students for a 10-week Quant Research Internship in 2027. You will analyze large financial datasets to feed into our end-to-end ML pipeline for equities, joining a small but established team with offices in London and New York.
You will collaborate on a structured data-driven project, receive mentorship, and gain exposure to our on-prem computational cluster and data library, along with feedback throughout the program.
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.
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.