Senior Data Scientist

Location: Brackenfell, Western Cape

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Job Title

Senior Data Scientist

Location - Town / City

Brackenfell, Cape Town

Location - Province

Western Cape

Location - Country

South Africa

Reporting To

Team Lead - Advanced Analytics

Job Category



To take a hands-on role in examining internal and external data to help improve revenue generation, reduce costs and operational performance through data analysis and where appropriate the creation of sophisticated algorithms or models.

This role is essential to support our Client''s efforts to increase the level of science it applies to its business practices in order to optimise profits in its mature markets. Initially the focus will be on optimising Shoprite''s relationship with its customers.



Hons or Master''s of Science (Msc) Degree in Statistics or other data mining related discipline (Mathematics, Operations Research)



    5 years experience in customer and/or retail analysis
    5 years experience in customer and/or retail data mining (statistical using R) with experience of deploying models in an operational environment
    2 years experience in at least three of the following:
  • Assortment optimisation
  • Customer driven marketing
  • Forecasting
  • Supply chain analytics
  • Clustering
  • Marketing mix modelling
  • Price optimisation
  • Product recommendation
  • Fraud detection
  • Workforce analytics



2 years - General Microsoft office skills
2 years - Strong understanding of data mining models, structures, theories, principles and practices
2 years - Expert knowledge of at least three of the following:
Decision trees
Association rules
Neural networks
Association rules


2 years - Skill in using statistical packages
2 years - Skills in using SQL in the data mining process, particularly for data preparation
2 years - Knowledge of Tableau software


See Knowledge

Job objectives

1. Understand best practices in data mining and business statistics for retail:
Monitor and analyse Shoprite''s competitors activity in data mining and business statistics
Monitor and analyse global trends in data mining and business statistics
2. Define models to be created and implemented together with the approach to implementing them:
Assist in defining the business problems with appropriate questions and by proposing a specific analysis approach
Identify the main concern of retailers and the appropriate modelling techniques to apply to them. Applications could include:
o Assortment optimisation and shelf space allocation
o Customer driven marketing
o Forecasting and other supply chain analytics
o Localisation and clustering
o Marketing mix modelling
o Price optimisation
o Product recommendation
o Fraud detection and prevention
o Workforce analytics
3. Explore data and create descriptive and predictive models in cycles:
Perform sophisticated data preparation in order to reduce and shape data
Apply visualisation and exploration techniques to the data
Apply a comprehensive set of predictive and descriptive modelling techniques to the data appropriate to achieving the business objective. This could include:
o Decision trees
o Association rules
o Neural networks
o Association rules
o Clustering

4. Put models into production, monitor for accuracy and robustness and where appropriate implement a retraining approach for models:
Where appropriate produce scoring and/or score cards required to put models into production
Monitor the performance of all models in production for their accuracy and robustness
Where appropriate implement a retraining approach for models
5. Assist in the evaluation and selection of appropriate technologies:
Work closely with solution architects, systems analysts and project managers in the design of solutions
Provide input on alternatives presented by the solution architects
Together with the data mining team engage the most appropriate stakeholders in the business and IT to obtain input and agreement on alternatives that are presented



1.Analysing and

*** Mention DataYoshi when applying ***

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