Quant Model Developer – Senior Associate
| Company | JP Morgan Chase |
|---|---|
| Location | Plano, TX, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior |
Requirements
- Bachelor’s or Master’s degree in a quantitative field such as Mathematics, Statistics, Economics, Finance, or Engineering.
- 3+ years experience in developing and implementing quantitative models in a financial or business setting.
- Strong understanding of statistical and mathematical modeling techniques.
- Familiarity with financial instruments, markets, and risk management practices.
- Strong problem-solving skills and the ability to work with complex datasets.
- Ability to interpret and communicate quantitative results to non-technical stakeholders.
- High level of accuracy and attention to detail in model development and data analysis.
- Excellent written and verbal communication skills for effective collaboration and documentation.
- Be a team player who shows commitment and dedication while maintaining a positive attitude and high level of performance on high profile/time-sensitive initiatives.
Responsibilities
- Design and implement quantitative models for pricing, risk management, and financial forecasting.
- Develop algorithms and statistical models to analyze financial data.
- Analyze large datasets to identify trends, patterns, and insights and use statistical tools and techniques to interpret complex data.
- Conduct back-testing and stress testing of models to ensure accuracy and reliability.
- Validate models against historical data and refine them as necessary.
- Prepare detailed documentation of model methodologies, assumptions, and limitations.
- Ensure compliance with regulatory requirements and internal policies.
- Work closely with product and risk managers and other stakeholders to understand business needs.
- Collaborate with IT teams to integrate models into existing systems.
- Stay updated with the latest developments in quantitative finance and modeling techniques and explore new data sources and methodologies to enhance model performance.
- Identify and assess potential risks associated with model assumptions and outputs while developing strategies to mitigate identified risks.
Preferred Qualifications
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No preferred qualifications provided.