Data Analyst

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.

Our Core Engineering department is seeking a Data Analyst to join our Data team in New York. 

Responsibilities

• Contributing to batch and real-time data pipelines that ingest, cleanse, and normalize structured and unstructured sources (market data, vendor feeds, web scrapes, alternative data)
• Writing and maintaining Python and SQL under the guidance of senior engineers
• Applying AI/ML methods to unstructured and semi-structured sources
• Prototyping prompts, models, and evaluation sets; helping productionize approaches that meet the firm’s accuracy bar
• Implementing and running validation checks, anomaly detection, and reconciliation logic
• Using statistical and ML-based methods to flag outliers and data breaks; investigate root causes and help prevent recurrence
• Assisting with onboarding new datasets: review vendor specs and sample files, map fields to internal models, and help integrate APIs under senior oversight
• Evaluating where LLMs or classical ML can accelerate mapping, documentation, and QA
• Using modern data tooling to monitor jobs, improve reliability, and document processes
• Building fluency in financial instruments, market data conventions, production engineering practices, and responsible use of AI on sensitive financial data