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Tudor Group

Low Latency Quantitative Researcher

New York City, Singapore

Location
New York City, Singapore
Work
Not stated
Employment
Not stated
Level
Not stated
Function
Data & AI
Pay
From SGD 150K a year (read from the ad)
Posted
17 months ago

About the role

Tudor’s Macro Pipeline team seeks a Quantitative Researcher to work within a low latency trading team that currently researches and builds low latency trading models in the liquid futures space. The candidate’s primary responsibilities will include researching and implementing fully automated systematic futures signals and strategies with short to medium horizon. Suitable candidates will generally have at least 3 years of comparable research experience.

Requirements

  • 3+ years of experience researching low latency futures signals and strategies
  • An advanced degree (MSc or PhD) from a top institution is preferred
  • Strong preference for advanced degrees in a quantitative field (e.g. Statistics, Machine Learning, Physics, Mathematics, or Engineering)
  • Excellent understanding of probabilities, statistics and optimization
  • Experience manipulating large datasets, including tick-level data
  • Excellent programing skills: experience with both high-level (e.g. Python, R, Julia) and lower-level languages (e.g. C, C++) with fluency in at least one.
  • High attention to detail
  • Creative thinker
  • Entrepreneurial spirit. Enjoys ownership of projects and takes responsibility for them

Compensation

  • Annual base salary for the position is expected to be from $150,000 per year to $250,000 per year. Actual salary offered to the successful candidate will depend on various factors including, but not limited to, geographic location, work experience and credentials, and/or skill level, the salary expectations of applicable applicants, and other market conditions. Details about eligibility for bonus compensation will be finalized at the time of offer.

Location

  • New York, NY, London, Singapore

Source

Posted by Tudor Group on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 01:43 UTC; the original is the reference.

View the original ad ↗

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