Finance Engineer Intern: Optimize Quant Models in NYC

London Stock Exchange Group; New York (NY); Hybrid; USD 56,000 - 93,000; Full time

Benefits offered by this job

Healthcare

Retirement planning

Wellbeing initiatives

Volunteer days

Commuter assistance

Job summary

The London Stock Exchange Group invites applicants for a Finance Engineer Summer Internship in New York City. You will join the Optimisation Strats Team within the Post Trade Engineering division, working on real problems and collaborating with engineers and the Product Development team to enhance our optimisation services.

Ideal candidates have strong Python skills with Pandas/NumPy familiarity, a solid mathematical background and an interest in linear/mixed-integer programming and convex

Qualifications

• Experience with Python and familiarity with Pandas and NumPy.
• Strong mathematical background (numerical methods, linear algebra, probability & statistics).
• Basic understanding of linear and/or mixed integer programming and convex optimisation.
• Excellent problem-solving skills.
• Knowledge of basic financial products is a plus.
• Familiarity with UNIX is desirable.
• Rights to work in New York or London for the duration of the internship.

Responsibilities

• Work on optimisation projects within the Strats team solving real problems.
• Collaborate with a 4–6 person team to design and implement improvements to optimisation services.
• Develop scaling schemes and mixed integer models to improve solution quality.
• Refactor code to share functionality across assets and reduce data/solution search space.
• Validate client data prior to optimisation and support product development feedback.

Skills

Python

Pandas

NumPy

Mathematics

Education

Undergraduate student (penultimate or final year)

Tools

UNIX

Job description

The London Stock Exchange Group invites applicants for a Finance Engineer Summer Internship in New York City. You will join the Optimisation Strats Team within the Post Trade Engineering division, working on real problems and collaborating with engineers and the Product Development team to enhance our optimisation services.

Ideal candidates have strong Python skills with Pandas/NumPy familiarity, a solid mathematical background and an interest in linear/mixed-integer programming and convex