Quantitative Analytics Lead

Enfusion is seeking a hands-on Quantitative Analytics Lead to spearhead the development and implementation of sophisticated models and methodologies aimed at the quantification of risk and measuring performance.  This position will play a critical role in the delivery of first-class risk services to our clients including cross-asset flexible, customizable tools and models that illustrate valuation transparency. You will be responsible for developing statistical and financial models relevant for market risk, valuation, and stress testing, and have the unique opportunity to interface with investment professionals while building new capabilities. 


What You'll Do:

• Participate in all aspects of the model life cycle, including design, implementation, testing, production, validation, and performance monitoring.
• Analyze our quantitative library and systems to identify performance inefficiencies and scaling opportunities.
• Interact with production clients on queries including price challenges, model/data validation requests, workflow testing, etc.
• 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
• Work with the support teams to answer complicated questions related to risk, performance, and valuation.
• Act as s an SME, 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.
  
  

What You'll Need:

• Masters or Ph.D. degree in a quantitative field such as statistics, applied mathematics, economics, quantitative finance, and operations research.
• 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).
• 10+ years of direct work experience in financial risk modeling, risk measurement and management, and regulatory capital requirements.
• Hands-on experience in the entire model development lifecycle including research, implementation, ongoing monitoring, and production maintenance.
• Experience in writing, editing, or reviewing technical documents suitable for regulatory or banking context.

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