Quantitative Researcher

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.

The Central Execution Desk is looking for a Quantitative Researcher to build models, analytics, and decision systems that improve execution quality across global trading teams. The role sits at the intersection of market impact research, transaction cost analysis, causal inference, experiment design, optimization, and execution strategy.

The researcher will work on understanding and improving how orders are routed, scheduled, evaluated, and optimized across brokers, algorithms, venues, markets, and trading teams. The work will combine rigorous quantitative research with practical execution-desk decision support, including market impact modeling, slippage analysis, causal inference, algo selection, broker evaluation, venue toxicity, A/B testing, and optimization under execution risk.

Responsibilities

• Researching market impact, execution cost, slippage, fill quality, etc. across global markets
• Building models to explain and predict execution outcomes
• Designing and analyzing A/B experiments to identify real execution improvements
• Working on causal inference methods 
• Developing optimization models for various execution objectives
• Supporting research into centralized inventory, liquidity, risk-transfer, and crossing-style analytics to improve portfolio-level execution outcomes