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Jump Trading

Campus Quantitative Researcher | Trading Team PhD/Postdoc (Full-Time)

New York, United States

Location
New York, United States
Work
Not stated
Employment
Full-time
Level
Not stated
Function
Data & AI
Pay
$200K – $250K a year
Posted
6 weeks ago

About the role

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

We are seeking a Quantitative Researcher with a strong academic background to a PhD level in a mathematical or science discipline. The role will involve you working closely with other team members to develop models and trading strategies in systematic macro.

What You'll Do

Quantitative Researchers collect and analyze tens of thousands of data sets, identify patterns and extract insights into the complexities in financial markets. Researchers lean heavily on statistical analysis, machine learning, and data engineering skills; applying the results of their research to forecasts and predictive trading models. Jump’s Quantitative Researchers are constantly collaborating with other scientists, traders, hardware and software developers, and market facing business teams to push for the best expression of our new ideas. You’ll own research projects end to end, in areas such as predictive modeling, alpha research on new datasets, and improving the models and systems behind live trading. Other duties as assigned or needed.

Skills You’ll Need

  • Currently pursuing a PhD in Statistics, Mathematics, Physics, Machine Learning, or any highly quantitative field
  • Systematic research thinking: the ability to form well-educated hypotheses, design rigorous tests, and draw statistically sound, generalizable conclusions. No matter your area, these are the fundamental aspects of a good researcher, and it is no different at Jump Trading.
  • Ownership of your research: the ability to explain the choices you made, the alternatives you considered and rejected, and why your approach won. Every idea demands a premise, and every rejection deserves a reason
  • Experience conducting an in-depth research project with real-world data
  • Programming experience in Python, with the ability to read, understand, and debug code, including code you didn't write
  • Communicative and collaborative working style, sharing results early and often and treating mentors' time as a resource to use, not conserve
  • Creativity and initiative to explore ideas beyond those suggested to you, with the judgment to bring your team along as you do
  • Perseverance: successful research is the result of lots of failure and intellectual risk-taking, and a PhD is often proof that you can stay with a hard problem for years without quitting
  • Fascinated and interested in advancing machine learning within the trading community
  • Desire to work within a collaborative, team-driven environment
  • Reliable and predictable availability

Benefits

  • Discretionary bonus eligibility
  • Medical, dental, and vision insurance
  • HSA, FSA, and Dependent Care options
  • Employer Paid Group Term Life and AD&D Insurance
  • Voluntary Life & AD&D insurance
  • Paid vacation plus paid holidays
  • Retirement plan with employer match
  • Paid parental leave
  • Wellness Programs

Annual Base Salary Range

$200,000—$250,000 USD

Source

Posted by Jump Trading on their own hiring page, www.jumptrading.com. OptiTalent shows it as published, read 07 Nov 2026 01:02 UTC; the original is the reference.

View the original ad ↗

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