Quantitative Trading & Research - Global Clearing - Associate
On-site · New York City, New York, United States
Job Summary
Quantitative Trading & Research Associate role focused on risk and pricing analytics for global clearing across F&O and OTC derivatives. Responsible for front-office risk/pricing analytics, margin solutions, model ownership and governance, production deployment of analytics and ML/AI solutions, and collaboration with Trading, QR peers, Technology, and Product Development. Emphasis on IM methodology, end-to-end delivery from problem framing to backtesting and scalable production, building robust analytics with APIs and services, maintaining documentation and reproducibility, and mentoring the team. Requires advanced quantitative degree, 5+ years of front-office quant experience, expertise in derivatives, Python/C++, SecDB/Athena/Quartz experience, model calibration and monitoring, and strong communication skills.
Required Qualifications
- Advanced degree (PhD, MSc, or equivalent) in Mathematics, Physics, Statistics, Computer Science, or a related quantitative field
- 5+ years of front-office quant experience supporting trading/risk in F&O and/or OTC derivatives
- Strong programming in Python and/or C++, with experience building high-performance, maintainable codebases
- Experience designing, calibrating, and maintaining IM/pricing models (curve construction, volatility surfaces, margin frameworks) including backtesting and performance monitoring
- Experience with front-office platforms such as SecDB, Athena, Quartz, or equivalent
- Excellent quantitative problem-solving abilities and ability to communicate uncertainty and trade-offs
- Outstanding communication and stakeholder management; ability to influence across QR, Trading, Technology, and Product
- Demonstrated mentorship or team leadership experience including setting technical direction and conducting code/method reviews
- Experience delivering production services with Technology partners (APIs, packaging, CI/CD, containerization, logging/monitoring)
- Knowledge of data governance, model risk governance, and regulatory expectations for pricing/risk models
- Hands-on ML/AI for quant finance (signal extraction, surrogate modeling, anomaly detection) with MLOps and drift monitoring
- Familiarity with distributed computing and market data tooling (e.g., kdb+/q, SQL) and performance engineering for large-scale simulations
- Contributions to research outputs (internal notes, publications, patents) and engagement with open-source scientific computing ecosystem
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