The Machine Learning Quantitative Analyst will work in our enterprise solutions area and work collaboratively to build a liquidity tool for banks, broker dealers, hedge funds, and other firms. You will be responsible for conducting statistical analysis, developing machine learning methodologies, model estimation and overseeing part of the research activities. Key responsibilities will also include exploring current academia and market best practices in machine learning approaches, accessing quality controls around different approaches and suggesting new approaches in research.
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. Experience in taking on independent research in developing a machine learning methodology from the ground up will be necessary.
You’ll need to have:
- An advanced degree in an applied numerical field: Mathematics, Statistics, Computer Science, Operations Research, etc.
- 1+ years of machine learning experience in professional role.
- A solid understanding of different machine learning techniques including: dimension reduction, manifold learning, and distance metric training
- 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: continuous and categorical.
- Experience with Python, R, or Matlab.
- Previously used SPARK, scikit-learn, etc.
- Parallel computing experience a plus.
- Communication skills both written and spoken.
- Financial industry experience
- Knowledge of Natural language processing
- Previous usage of Scala
- Parallel computing experience