Quantitative Analytics Lead

2 days ago

London, Greater London, United Kingdom Enfusion Full time
About the Role

Enfusion is seeking a highly skilled Quantitative Analytics Lead to spearhead the development and implementation of sophisticated models and methodologies aimed at the quantification of risk and measuring performance.

Key Responsibilities
  • Model Development and Implementation: Participate in all aspects of the model life cycle, including design, implementation, testing, production, validation, and performance monitoring.
  • Quantitative Analysis: Analyze our quantitative library and systems to identify performance inefficiencies and scaling opportunities.
  • Client Interaction: Interact with production clients on queries including price challenges, model/data validation requests, workflow testing, etc.
  • Sales Support: Work with the sales team in new client engagements, often involving in-person client visits as well as conducting in-depth product testing and coverage checks/validation.
  • Technical Expertise: Act as a subject matter expert, collaborating with internal stakeholders, including engineers and product owners to drive new enhancements in our valuation/data platform, and understand technical requirements to implement high-performance code enhancing analytics infrastructure.
Requirements
  • Education: Masters or Ph.D. degree in a quantitative field such as statistics, applied mathematics, economics, quantitative finance, and operations research.
  • Technical Skills: Strong quantitative skills and deep understanding of the following technical areas: 1) financial mathematics (e.g., derivatives pricing models, stochastic calculus, advanced linear algebra); 2) econometrics (e.g., time series analysis, copulas, etc.) and machine learning techniques; and 3) numerical methods and optimization (e.g., Monte Carlo simulation and finite difference techniques).
  • Experience: 10+ years of direct work experience in financial risk modeling, risk measurement and management, and regulatory capital requirements.
  • Model Development: Hands-on experience in the entire model development lifecycle including research, implementation, ongoing monitoring, and production maintenance.
  • Technical Documentation: Experience in writing, editing, or reviewing technical documents suitable for regulatory or banking context.

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