Oil Derivatives Trading – Quantitative Researcher Oil Derivatives Trading – Quantitative Researcher …

Gunvor Singapore Pte Ltd
in Singapore
Permanent, Full time
Last application, 17 Jun 21
Competitive
Gunvor Singapore Pte Ltd
in Singapore
Permanent, Full time
Last application, 17 Jun 21
Competitive
.

Overview of Role

The person will join the Cross Barrel Desk as a Quantitative Researcher. This is an exciting opportunity for individuals with experience in quantitative field, looking to join an innovative trading team and apply their knowledge in commodity market. The candidate will focus on the development of quantitative tools to allow traders to back test trading ideas – the task will encompass the on-boarding of the relevant datasets, the development of Machine Learning techniques to extract information from those datasets and the control of the overfitting risk by using various statistical techniques

Based on the location of the identified candidate, this role could be based in our Singapore or Geneva offices (preferred locations), with a possibility to be based in Houston.

Main Responsibilities

Joining as a Cross Barrel Quantitative Researcher, the candidate will

  • Develop quantitative tools to automate quantitative analysis study
  • Work with traders to design and develop trading strategies whilst controlling the overfitting risk using various statistical techniques
  • Develop and enhance the existing analytical tools and databases
  • Enhance the desk portfolio construction algorithm by adding new features or instruments
  • Improve execution algo within the desk automated execution platform
  • Analyze trade execution algorithms to minimize market impact
  • Proactively implement market best practice development cycle (Dev/Test/Prod)

Profile

The individual should have the following experience:

  • 2-5 years of relevant experience
  • Proficiency in Python is a must
  • Strong programming skills (SQL and MongoDB, C++/ C# and Java)
  • Strong knowledge in statistics and modeling background (including Classification Algorithms such as Random Forrest, Machine Learning, etc.)
  • Experience with high performance database for time-series data is preferred
  • Knowledge of financial markets and financial derivatives would be a plus but is not required
  • Excellent verbal and written communication skills in English
  • Inquisitive and meticulous with strong analytical and computational skills
  • Self-motivated and able to work independently and as a team player
  • Ability to work under pressure
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