VP, Quant Trader

BNP Paribas offers you an exciting career opportunity in an international, challenging business environment characterized by high pace and diversity with focus on creating valuable relations with our customers. We offer a competitive salary & benefits package and also an excellent work environment where you’re valued as part of our team!

We are looking to expand our e-FX systematic trading and Quantitative trading team globally. The candidate will be able to implement systematic and high frequency trading strategies using our in-house research framework that will aim to offer liquidity in order to hedge our risks. We have one of the best bespoke research tools in the market to generate alpha and create new signals.
The Role
  • Working within the firm's automated trading framework
  • Designing, implementing and deploying high-frequency trading algorithms in e-FX.
  • Run and analyze backtests on these strategies using internal tools and the analytics library
  • Conduct analysis of market data and market microstructure for patterns in order to explore trading ideas
  • Contribute to libraries of analytical computations to help grow and support market data analysis and trading
  • Integrate the generated alpha into pricing and hedging for our e-commerce franchise
  • Enhance our pricing and skew performance using statistical approach and methods.
  • Work with Quant teams worldwide on global topics and projects
Skills/Requirements
  • Must have min 3+ years of experience as Quant Trader and a proven track record is an advantage
  • A strong academic background with a Master Degree in Financial Engineering, Mathematics, Physics, Computer Science, Statistics or Operations Research
  • Should be autonomous in coding and know R and SQL, C++ in Linux environment
  • Must be able to communication with colleagues from different locations
  • Work with IT Strategists locally and globally
  • Must be able to multitask and work under pressure
  • Proficiency in back-testing, simulation and statistical techniques (regression, correlation, alpha generation..etc)
  • Having strong data-mining and analysis skills, including experience with large data/tick data,