Quantitative Researcher, Central Execution Desk Tower Research Capital · New York, United State[...]

Tradermath; New York (NY); On-site; USD 120,000 - 200,000; Full time

Benefits offered by this job

Hybrid working opportunities

Generous paid time off

Wellness reimbursements

Office snacks

Team events

Job summary

Tower Research Capital seeks a Quantitative Researcher to build models and analytics that improve execution quality across global trading teams. You will tackle market impact, slippage, sequencing, and optimization with rigor and practical desk support.

The role blends theoretical research with production-ready analytics, collaborating with traders, PMs, and engineers to deploy robust decision systems. Strong Python and optional C++/Rust are valued.

Qualifications

• Masters or Bachelors from a top-tier university in mathematics, statistics, computer science, financial engineering, physics, operations research, or a related quantitative field. PhD preferred.
• 2-5 years of quantitative research experience, ideally in execution research, market microstructure, or financial data modeling.
• Strong knowledge of statistics, time-series analysis, experiment design, optimization, machine learning, and financial markets.
• Experience working with large financial datasets.
• Strong Python skills for research, data analysis, modeling, and simulation. C++/Rust experience is a plus but not required.
• Familiarity with convex optimization, causal inference, market impact models, stochastic control is a plus
• Good understanding of market microstructure
• Ability to write clear research notes and explain quantitative results to traders, PMs, and engineers.

Responsibilities

• Research market impact, execution cost, slippage, fill quality across global markets.
• Build models to explain and predict execution outcomes.
• Design and analyze A/B experiments for real execution improvements.
• Work on causal inference methods.
• Develop optimization models for execution objectives.
• Support research into inventory, liquidity, risk-transfer, and crossing analytics to improve portfolio-level outcomes.
• Contribute to research on execution algorithms.
• Build research tools, simulations, dashboards, and reports for traders and PMs.
• Collaborate with traders, PMs, quant developers, and engineers to deploy production analytics.

Skills

Python

Statistics

Time-series analysis

Experiment design

Machine learning

Education

Masters or PhD in quantitative field

PhD preferred

Tools

C++

Rust

Job description

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
• Contributing to research on execution algorithms
• Building research tools, simulations, dashboards, and reports that help traders and PMs make better execution decisions
• Partnering with traders, PMs, quant developers, and engineers to turn research prototypes into robust production analytics.

Qualifications

• A Masters or Bachelors from a top-tier university in mathematics, statistics, computer science, financial engineering, physics, operations research, or a related quantitative field. PhD preferred
• At least 2-5 years of quantitative research experience, ideally in execution research, market microstructure, or financial data modeling
• Strong knowledge of statistics, time-series analysis, experiment design, optimization, machine learning, and financial markets
• Experience working with large financial datasets
• Strong Python skills for research, data analysis, modeling, and simulation. C++/Rust experience is a plus but not required
• Familiarity with convex optimization, causal inference, market impact models, stochastic control is a plus
• Good understanding of market microstructure
• Ability to write clear research notes and explain quantitative results to traders, PMs, and engineers. Prior academic publications or comparable research writing are a plus

Anticipated annual base salary range $120,000 - $200,000, plus eligible for discretionary bonus.

Benefits

Tower's headquarters are in the historic Equitable Building, right in the heart of NYC's Financial District and our impact is global, with over a dozen offices around the world.

At Tower, we believe work should be both challenging and enjoyable. That is why we foster a culture where smart, driven people thrive - without the egos. Our open concept workplace, casual dress code, and well-stocked kitchens reflect the value we place on a friendly, collaborative environment where everyone is respected, and great ideas win.

Our benefits include:

• Generous paid time off policies
• Savings plans and other financial wellness tools available in each region
• Hybrid working opportunities
• Free breakfast, lunch, and snacks daily
• In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
• Company-sponsored sports teams and fitness events (JPM Corporate Challenge, Cycle for Survival, Wall Street Rides FAR and more)
• Volunteer opportunities and charitable giving
• Social events, happy hours, treats, and celebrations throughout the year
• Workshops and continuous learning opportunities

At Tower, you'll find a collaborative and welcoming culture, a diverse team and a workplace that values both performance and enjoyment. No unnecessary hierarchy. No ego. Just great people doing great work - together.

Tower Research Capital is an equal opportunity employer.