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. 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 Taking ownership of well-scoped datasets and processes as you ramp Qualifications Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, Physics, Economics, or a related quantitative discipline Strong proficiency in Python and SQL. Evidence of this through coursework, thesis/research code, internships, or personal projects Internship or research experience involving data engineering, quantitative research, market data, or financial datasets Practical experience with LLMs for unstructured data processing (document/entity extraction, classification, summarization) and with evaluation harnesses or human-in-the-loop review Coursework or project experience with workflow tools (Airflow, Dagster), cloud platforms (AWS/GCP), or warehouses (Snowflake, BigQuery, Databricks). Exposure to financial instruments, market microstructure, or vendor datasets (Bloomberg, S&P, LSEG) Prior work in a collaborative research lab or production software setting Hands-on experience with machine learning or applied AI through coursework, thesis/research, internships, or projects Comfortable with at least one of: classical ML (scikit-learn or similar), NLP, or LLMs (prompting, evaluation, structured extraction) Demonstrated ability to work with messy, large, or imperfect datasets: cleaning, validating, summarizing, and drawing defensible conclusions Familiarity with Linux or Windows command line, version control (Git), and basic software engineering hygiene (testing, documentation, code review) Clear written and verbal communication Ability to explain technical and AI-related findings to both engineers and non-technical stakeholders, including limitations and failure modes Strong problem-solving skills, intellectual curiosity, and a bias toward getting details right Comfortable working in a fast-paced, high-accountability environment Anticipated annual base salary range USD $150,000 - $180,000 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.

Requirements

Must-haves

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.
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
Taking ownership of well-scoped datasets and processes as you ramp
Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, Physics, Economics, or a related quantitative discipline
Strong proficiency in Python and SQL. Evidence of this through coursework, thesis/research code, internships, or personal projects
Internship or research experience involving data engineering, quantitative research, market data, or financial datasets
Practical experience with LLMs for unstructured data processing (document/entity extraction, classification, summarization) and with evaluation harnesses or human-in-the-loop review
Coursework or project experience with workflow tools (Airflow, Dagster), cloud platforms (AWS/GCP), or warehouses (Snowflake, BigQuery, Databricks).
Exposure to financial instruments, market microstructure, or vendor datasets (Bloomberg, S&P, LSEG)
Prior work in a collaborative research lab or production software setting
Hands-on experience with machine learning or applied AI through coursework, thesis/research, internships, or projects
Comfortable with at least one of: classical ML (scikit-learn or similar), NLP, or LLMs (prompting, evaluation, structured extraction)
Demonstrated ability to work with messy, large, or imperfect datasets: cleaning, validating, summarizing, and drawing defensible conclusions
Familiarity with Linux or Windows command line, version control (Git), and basic software engineering hygiene (testing, documentation, code review)

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningpythonsqlaimlawsgcp

Platform: greenhouse
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Benefits

Benefits detected in the listing.

Paid Time Off

Wellness