12 months ago
My client is pioneering the use of statistical, econometric, and machine learning methods in helping to calculate liquidity risk. If you are a highly motivated and enthusiastic individual this is a very unique opportunity to contribute to the development of an innovative regulatory product using rich financial data and multiple machine learning techniques.
You will work the businesses Enterprise Solutions area and work collaboratively to build a liquidity tool for banks, broker dealers, hedge funds, and other firms. You will need to show special attention to data integrity and robustness of various models, a rigorous scientific/statistical approach and a complete IT background.
We’ll trust you to:
- Contribute original research and be hands-on in the development of an innovative regulatory product
- Conduct statistical analysis, developing machine learning methodologies, model estimation and leading part of the research activities
- Explore current academia and market best practices in machine learning approaches
- Assess quality controls around different approaches and suggesting new approaches in research
- Use independent research in developing machine learning methodology from the ground up
You’ll need to have:
- An advanced degree in an applied numerical field: Mathematics, Statistics, Computer Science, Operations Research, etc.
- 2+ years of financial market experience in professional role. Experience with market structure of different asset classes is a plus
- Hands on experience modelling securities from different asset classes (e.g. Rates, Vol, Futures). Modelling can be done from a risk, microstructure, execution, or alpha generation perspective
- A solid understanding of different statistical, econometric and machine learning techniques including: dimension reduction, manifold learning, and distance metric training, and classification
- Strong quantitative analysis, programming, and statistical modeling skills
- A track record of gathering, matching, and pre-processing large data sets from varied sources and of different characteristics
- Experience in the analysis on mixed features in modelling: continuous and categorical.
- Experience with Python, R, or Matlab
We’d love to see:
- Previous experience using SPARK
- Parallel computing experience