Quantitative Trader
Jane Street Group, LLC - Hong Kong, Hong Kong
Posted Jan 16, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
- Not verified
- Verification
- Not verified
- Salary
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- 401(k) match
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Schedule
- Shift type
- Not verified
- Weekend work
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Application
- Cover letter
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- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Quantitative Trader Hong Kong, Hong Kong About the position Our goals are to give you a real sense of what it's like to work as a quantitative trader at Jane Street while also providing a truly unparalleled educational experience. You'll be paired with experienced quantitative traders who will teach you how to identify market signals, analyze and execute strategies, construct quantitative models, conduct statistical analysis, and build trading intuition. You'll work closely with two different mentors on projects relating to their day-to-day work, giving you a sense of the variety of problems we solve every day. Past projects have included analyzing new or existing datasets, training predictive models, simulating potential new trading strategies, writing tools that we use in production, and even working to answer big-picture questions we haven't yet figured out. This work is reinforced with intensive classes, workshops, and team-based mock trading sessions. These will expose you to many of the dynamics we observe in real markets, illustrate the role that we play in making markets more efficient, and help build intuition for how we think about both trading and collaborating. At Jane Street, the lines between research, technology, and trading are intentionally blurry. As our strategies grow more sophisticated, close collaboration is essential for continuing to push the boundaries of what's possible. We work with petabytes of data, have a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don't believe in “one-size-fits-all” modeling solutions; we are
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