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Remote Quantitative Developer Intern information
What does a Remote Quantitative Developer Intern do?
What are some common challenges faced by Remote Quantitative Developer Interns, and how can they overcome them?
What are the key skills and qualifications needed to thrive as a Remote Quantitative Developer Intern, and why are they important?
What is the difference between Remote Quantitative Developer Intern vs Quantitative Analyst Intern?
| Aspect | Remote Quantitative Developer Intern |
|---|
| Required Credentials |
|---|
| Typically pursuing or holding a degree in Computer Science, Mathematics, or related fields; coding skills essential |
| Work Environment |
|---|
| Remote, collaborative teams within financial firms or hedge funds |
| Employer & Industry Usage |
|---|
| Commonly employed in quantitative trading firms, hedge funds, or financial technology companies |
| Comparison Summary |
|---|
The Remote Quantitative Developer Intern focuses on coding, developing algorithms, and building trading models, requiring strong programming skills. In contrast, a Quantitative Analyst Intern typically emphasizes data analysis, statistical modeling, and research. Both roles often require similar educational backgrounds and are found in financial industries, but their core responsibilities differ, with the developer role being more technical and programming-oriented.
Full-time
This job post has expired today. Applications are no longer accepted.
Job description
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.