Reporting to Team Lead: Analytics, the Data Scientist will build novel new applications to enable & assist data-driven decision making using advanced data science techniques. Areas of focus includes but is not limited to behaviour prediction, image recognition & natural language processing.

These new applications are typically high profile & have an impact on revenue generation & cost reduction. The client is focused on customer service & operational efficiency.There is a rapidly increasing demand for scientific methods to apply to business practices to support this.This role is essential in growing the data science capability required to meet this need.

You Need:

• Post grad Science degree (Data Science, Mathematics/Computer Science.
• Solid customer and/or retail analysis
• Solid Customer and/or retail data science experience with focus on neural networks while building & putting into production applications that leverage these models
• Good exp in at least three of the following:-
  • Customer behaviour prediction
  • Assortment optimisation
  • Customer driven marketing
  • Supply chain analytics
  • Recommendation engines
  • Image recognition
  • Natural Language
  • Digital assistants
• Python for data analysis & building neural networks
• SQL & scripting tools (RegEx, Awk, Shell scripting) in the data mining & cleansing process, particularly for data preparation
• Neural network framework such as Keras or TensorFlow
• Strong understanding of data science theories, principles & practices
• Expert in at least two of:-
  • Decision trees
  • Association 
  • Transfer learning 
  • Clustering
• Some Development skills in Java, C++, Scala, NodeJS

TO DO

• Lead the requirements discovery process in a retail environment
• Seek out, explore & manage data relevant to the requirements 
• Create models using data science techniques, particularly focusing on neural networks 
• Own the development of a complete application that exploits the model 
• Own the process of deploying the data science application & gaining its acceptance by the business
• Own ongoing quality of the solution in terms of data, model & performance