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Quantitative Developer Remote Jobs (NOW HIRING)

A forward-thinking AI development team seeks quantitative professionals to evaluate AI-generated work, contribute to model improvement, and perform statistical analysis. This fully remote role offers ...

Quantitative Researcher

$150K - $200K/yr

S. Remote *Telecommuting permitted: work may be performed in any location in the U.S. DUTIES ... Master's degree (US or Foreign Equivalent) in Financial Engineering or a related field and two (2) ...

... remote-first environment, you will collaborate with globally distributed teams across engineering ... Design and develop quantitative methodologies that combine multiple market and reference data ...

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Quantitative Developer Remote information

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$98K

$169.7K

$259.5K

How much do quantitative developer remote jobs pay per year?

As of May 29, 2026, the average yearly pay for quantitative developer remote in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.
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Full-time

Posted 15 days ago


Job description

Senior Quant Developer
Remote USA
Mandatory skills: Python OR C++, Monte Carlo simulation, quantitative background, derivatives pricing models and risk model back testing experience
Skills:
Minimum degree of Master or PhD in quantitative fields is required, with at least 3-5 years of relevant experience.
The candidate must have strong quantitative and analytical background with a solid theoretical foundation coupled with strong programming, documentation and communications skills.
Must have experience implementing complex market or credit risk quantitative modelling for OTC derivatives using programming languages (such as Python and C++) as well as mathematical/statistical software packages.
Knowledge of derivatives pricing models (Black Scholes, Hull White), Monte Carlo simulation, and risk model back testing experience is also a must.
Nice to have:
The candidate is preferred (a plus) to have experience in credit risk modelling and is familiar with credit risk concepts such as PFE (Potential Future Exposure), CSA, MPOR, collaterals IM and VM, and Monte Carlo simulation of long-time horizons.