Financial Data Science Specialist

4 days ago


Randburg, Gauteng, South Africa NTT Data Full time

Job Overview:

We are seeking a talented Equity Market Model Developer to join our team at NTT Data.

About the Role

This is an exciting opportunity for a skilled data scientist to contribute to the development of cutting-edge market-making models. As an Equity Market Model Developer, you will play a critical role in helping us deliver innovative solutions to our clients in the financial services sector.

Responsibilities:

  • Develop and optimize 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.
Requirements

To succeed in this role, you must have:

  • A minimum 5-7 years of experience in data analytics, machine learning, quantitative analysis.
  • Proven experience working in complex financial systems and applications (e.g., trading platforms, payment systems, risk management systems).
  • A strong understanding of financial products, capital markets, derivatives, and treasury operations.
  • Strong knowledge of equities and trading.
  • 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.
  • Hands-on experience with coding in Python and the use of visualization tools.
  • Must have experience with machine learning.
  • Must have knowledge of market microstructure.
  • Experience with high-frequency trading data and market microstructure.
  • Proficiency in Python and familiarity with AWS services, especially Sage Maker.
  • Ability to work with large datasets and perform data normalization.
  • Knowledge of technical indicators and their application in equity markets.


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