Quantitative Research

Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization.

Engineers thrive at Tower while developing electronic trading infrastructure at a world-class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration, and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability, and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

As an intern on our Central Execution Desk you will be working on cutting edge execution research, modeling, and execution performance evaluation.

Responsibilities

• Exploring execution and trading ideas by analysing market microstructure for patterns in liquidity, trading costs, and execution outcomes. 
• Building tools, simulations, and analytics to support trading and execution decisions
• Contributing to research libraries and production workflows for execution analysis
• Designing and evaluating models; performing causal analysis
• Working with large, noisy datasets and building robust data-processing and model-calibration pipelines 

Qualifications

• A current student from a top-tier university pursuing a PhD in Mathematics, Statistics, Computer Science, Financial Engineering, Physics, Operations Research, Economics, or an equivalent quantitative discipline
• A strong grounding in mathematics, statistics, optimization, and machine learning
• In depth data-mining and analysis skills
• Strong programming skills in one or more of Python, C++ and Rust