Senior Data Scientist

3 days ago

Johannesburg, Gauteng, South Africa Boardroom Appointments Full time R1 200 000 - R2 400 000 per year


Minimum Requirements:

  • Matric (Grade 12)
  • 5-7 years of experience manipulating data sets and building statistical models. 
  • Having a degree in one of the following fields, Mathematics, Computer Science or another quantitative field is advantageous.
  • Strong problem-solving skills with an emphasis on product development.
  • Excellent written and verbal communication skills for coordinating across teams.
  • Proven track record of implementing ML systems, using TensorFlow and
    PyTorch.
  • Strong experience
  • Using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
  • Querying databases/datasets
  • Statistical and data mining techniques: GLM/Regression, Rando Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Visualizing/presenting data for stakeholders using: D3, ggplot, Kibana, Olik, Sisense etc.
  • A variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

Responsibilities:

  • Work with stakeholders throughout the organization to identify
    opportunities for leveraging company data to drive business solutions.
  • Mine and analyze data from company databases to drive optimization
    and improvement of product development, marketing techniques and
    business strategies.
  • Assess the effectiveness and accuracy of new data sources and data
    gathering techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences,
    revenue generation, ad targeting and other business outcomes.
  • Develop company A/B testing framework and test model quality.
  • Coordinate with different functional teams to implement models and
    monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance
    and data accuracy.

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