Data Analyst

Tower Research Capital is a quantitative trading firm with engineering, trading, and business support teams. The company is seeking a Data Analyst to contribute to data pipelines, apply AI and machine learning methods to financial and alternative data, validate and monitor datasets, and support the onboarding and productionization of data processes in collaboration with senior engineers.

What you'll do:

• 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

What they're looking for:

• 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

Benefits:

• 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
• Open concept workplace