VP/SVP Algo Trading Model Validation VP/SVP Algo Trading Model Validation …

Selby Jennings QRF
in Manhattan, NY, United States
Permanent, Full time
Be the first to apply
USD175000 - USD175001 per year
Selby Jennings QRF
in Manhattan, NY, United States
Permanent, Full time
Be the first to apply
USD175000 - USD175001 per year
Selby Jennings QRF
A leading bulge bracket bank is looking to hire a model validator on their newly built out team responsible for algorithmic trading models covering all asset classes.

A leading bulge bracket bank is looking to hire a model validator on their newly built out team responsible for algorithmic trading models covering all asset classes. This group has grown extensively over the past couple of years, supporting the push for algo trading models and is looking for an experienced model validator to join the group at the VP/SVP level. This person needs to have a strong statistical modeling background, and extensive experience working in model validation or model development using statistical methods. This is a great opportunity located in the New York Area.

Responsibilities:

  • Developing, enhancing, and validating the methods of measuring and analyzing risk for algorithmic trading models that cover all asset classes
  • Develop models and oversee model development, validation, and deployment efforts
  • Advancing Risk Management methodology as it pertains to algorithmic trading
  • Managing successful annual quantitative and qualitative assessments and submissions
  • Working with large datasets and complex algorithms to solve data science challenges
  • Introducing cutting edge Model technique to drive profitability

A Successful Candidate Will Have:

  • ~5-8 years of relevant, full time experience
  • Prevalent knowledge of statistical modeling concepts and industry best practices
  • Experience with econometric and statistical modeling or application risk scoring
  • Experience working with analytical or data manipulation tools
  • Excellent quantitative and analytic skills
  • Preferably a PhD in Statistics or a closely related quantitative discipline, but at least a masters degree is required
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