Vacancy detail

Credit Algo Quant - AVP/VP level

$120,000 – $220,000 performance related bonus

Location: United States (New York – New York) Type: Permanent

Credit Algo Quant - AVP/VP level

NYC based

Develop and implement electronic market-making models for the Credit and mortgage businesses.


  • Research and implement electronic market-making models (hedging algorithms, bid-offer models, price predictors, automated pricing models)
  • Analyze performance of the model with actual market-making operation and issue recommendations for model improvements
  • Build hedging strategies

While, the importance of having an analytical and quantitative mindset is imperative for success in this job, you must also be practically minded in that you must understand how models generate value for the trading operation, what level of sophistication they should have in order to achieve their goals while remaining simple and easy to implement, and how model performance can be evaluated, both in testing and in production. Ability to test models thoroughly and implement the appropriate ones should be of primary focus. The position requires both attention to detail and the ability to see the "big picture" of the trading operation. You should have exemplary quantitative skills combined with an ability to explain models in simple terms.

This role offers the possibility to learn the microstructure of the Credit markets and Mortgage markets at a time when the role of electronic trading and systematic strategies is rapidly increasing. It also gives the opportunity to work in a closely integrated team comprised of traders, quants and technologists, with exposure to all aspects of the operation.


  • Proven success in solving practical problems (ideally in finance) using statistical and machine learning techniques is essential.
  • Knowledge and experience in machine learning based modeling is a must.
  • Experience working in a systematic trading field (execution algorithms/statistical arbitrage/electronic market making) is preferred.
  • Familiarity with credit or mortgage products and market microstructure would be ideal.
    Strong programming skills in C++ and Python.
  • Proficient in database applications (for example, SQL or KDB).
  • Ability to work with large data-sets.
  • S. or Ph.D. in mathematics, physics, statistics, engineering or financial engineering.


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