Specialist Data Scientist

Description:

The Global Markets Data Analytics department focuses on enhancing data quality and analytics capabilities within the organisation. The team collaborates closely with various departments, including Sales, Trading, and IT, to ensure data integrity and provide actionable insights. The team is responsible for managing data from various sources, including Mopani, Refinitiv, Bloomberg, and others, to support electronic trading and market analysis.
Assist in the development and optimization of market-making models, focusing on equities and equity derivatives. Conduct back testing and research to improve current models and develop new ones. Analyse high-frequency trading data to identify patterns and trends. Implement machine learning techniques, particularly LSTMs and convolutional networks, to predict short-term price movements. Collaborate with teams to integrate new indicators and models into production environments. Work with AWS infrastructure, including S3 buckets and Sage Maker, for data processing and model training, or possibly an alternative similar platform. Generate and analyse data samples, including time-based and volume-based sampling. Ensure the robustness of models by balancing in-sample and out-sample results. Key Projects: Expanding the universe of stocks traded by the current algorithm, creating new algorithms and possibly updating current sell-side / trader algos (market making). Incorporating trading indicators and market trends into existing models. Developing stationary indicators to predict price movements. Collaborating with the data analytics team to optimize model performance.
· Assist in the development and optimization of market-making models, focusing on equities and, equity derivatives.
· Conduct back testing and research to improve current models and develop new ones.
· Analyse high-frequency trading data to identify patterns and trends.
· Implement machine learning techniques, particularly LSTMs and convolutional networks, to predict short-term price movements.
· Collaborate with teams to integrate new indicators and models into production environments.
· Work with AWS infrastructure, including S3 buckets and Sage Maker, for data processing and model training.
· Generate and analyse data samples, including time-based and volume-based sampling.
· Ensure the robustness of models by balancing in-sample and out-sample results.
Preferred Qualifications:
· Familiarity with cloud technologies and their application within the financial services sector.
· Strong background in machine learning, particularly in time series analysis and neural networks.
· Experience with high-frequency trading data and market microstructure.
· Proficiency in Python and familiarity with AWS services, especially Sage M