VP Quantitative Researcher - Lending & Risk Analytics

Fairygodboss; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorganChase is seeking a senior quantitative professional to lead development of lending and risk models within the Investment & Research Team. The role involves empirical risk research, model benchmarking, and close collaboration with technology and data teams to enhance analytics for wealth management assets.

The candidate will apply advanced probability theory and risk concepts, with a focus on margin and collateral valuation impacts on decision-making and P&L.

Qualifications

• PhD, MSc, or equivalent in a quantitative field such as Mathematics, Physics, Statistics or CS.
• 7+ years of quant experience with strong knowledge of probability, risk, VaR and model assumptions.
• Experience with lending, margin, and financial products (equity, fixed income, commodities, HF, private equity, currency, derivatives).
• Knowledge of wealth management, risk controls and regulatory considerations.
• Strong Python coding skills and ability to explain complex concepts to regulators and senior management.

Responsibilities

• Design, develop, and enhance quantitative models and analytics for lending value estimations.
• Identify key risk drivers and incorporate them into model design.
• Establish model performance benchmarks and accuracy measures.
• Build domain expertise in collateral operations, margining workflows, and collateral valuation impact on decisions and P&L.
• Support product strategy by evaluating trade-offs and quantifying methodology changes.
• Collaborate with Technology and data teams to source, reconcile, and curate data.

Skills

Advanced degree in quantitative field

Quant experience

Python coding

Regulatory communication

Education

PhD/MSc in Mathematics/Physics/Statistics/CS

Tools

Python

Job description

JPMorganChase is seeking a senior quantitative professional to lead development of lending and risk models within the Investment & Research Team. The role involves empirical risk research, model benchmarking, and close collaboration with technology and data teams to enhance analytics for wealth management assets.

The candidate will apply advanced probability theory and risk concepts, with a focus on margin and collateral valuation impacts on decision-making and P&L.