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We are currently working with one of the leading investment banks who are looking to expand their data scientist team whose primary focuse is on alternative data sets. The information extracted will be used to construct strategies across multiple asset classes, including futures, equities and foreign exchange.
The data scientist team is massively expanding and are looking to take on experienced data scientists. They are specifically interested in data scientists with experience in machine learning techniques (both supervised and unsupervised), as well as experience in analyzing non-traditional data sets.
You will be part of an exciting team that will be building the trading systems for both external clients as well as internal traders. This is through discovering and modelling predictable structures in the large amounts of structured and unstructured non-traditional data collections (satellite imagery, credit card transactions, etc).
The role entails both research and analysis from a variety of non-traditional data sets, then using the information gained to develop strategies and forecasting models using a variety of machine learning and econometric techniques. The strategies that the data science team formulate will be implemented on new execution, trading, risk management and portfolio construction applications.
- PhD or PhD candidates in STEM subjects, machine learning, computer science, or related areas.
- Hands on experience with non-traditional data sets.
- Financial experience is not required
- Published work is very desirable, particularly in relevant fields
- Interest in financial markets
- Machine learning experience in areas such as computer vision or general machine learning background