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Quantitative Trader / Researcher - US

Tower Research Capital
🇺🇸New York🇺🇸Charleston🇺🇸ChicagoJuniorFull-time$120k–$200kPosted July 20, 2026

About the Role

As a Quantitative Trader / Researcher at Tower Research Capital, you will develop and deploy algorithmic trading strategies based on patterns in market behavior using Tower's in-house trading system—one of the fastest and most comprehensive in the world. Your responsibilities will include designing, implementing and deploying trading algorithms, researching high to mid frequency alphas, and exploring trading ideas by analyzing market data and alternate data for patterns. You will also create tools to analyze data for patterns, develop data platforms, contribute to analytical computation libraries, and develop exchange simulators.

Requirements

  • A Bachelors, Masters or PhD degree in Mathematics, Statistics, Computer Science or equivalent STEM degree
  • Experience in quantitative trading is preferred but not a requirement
  • A strong background in mathematics and statistics
  • Proficiency in back-testing, simulation, and statistical techniques (auto-regression, auto-correlation and Principal Component Analysis)
  • Solid data-mining and analysis skills, including experience dealing with a large amount of data/tick data
  • Familiarity with signal generation and statistical models
  • Strong programming skills in Python or C++

What You'll Get

  • $120k–$200k base salary
  • 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