Machine Learning Engineer

Position Purpose:

The Machine Learning Engineer is primarily responsible for building end-to-end machine learning models from ideation to deployment and scalability. You would create new and improved data-driven solutions to assist the Group in answering business questions, gaining competitive advantage, identifying new market opportunities, increasing efficiencies, and/or reducing costs.

 

Qualifications:

  • 4 year Degree / NQF level 7 in IT and Computer Sciences

 

Experience:

  • At least 3 years of Machine Learning experience
  • At least 3 years of Data Science experience

Knowledge & Skills:

  • SQL
  • Python
  • Cloud Platform (AWS) advantageous
  • Probability and Statistics
  • Prescriptive Modeling

Job objectives:

  • Work in a cross-functional team, collaborating with data scientists, engineers, and analysts to understand project goals, interpret end-users intent and drive the build, implementation and scale-up of algorithms for measurable impact.
  • Understand and use ANN’s, CNN’s, RNN’s, auto encoders, fundamental data science concepts (linear and logistic regression, SVM’s, dimensionality reduction), decision trees, gradient boosting, ensemble models, etc. to develop machine learning models.
  • Implement above architectures with deep learning frameworks such as Keras and Tensor Flow.
  • Train models on large-scale data and fine tune hyper-parameters.
  • Research and implement appropriate machine learning algorithms and tools by selecting the correct libraries, programming languages, and frameworks for each task.
  • Understand and use computer science fundamentals, including data structures, algorithms, computability, and complexity and computer architecture.
  • Keep abreast of developments in the field, and integrate the latest data technologies into existing requirements.
  • Follow best practices and standards in machine learning.
  • Peer review machine learning models, and advise on shortfalls and improvement.
  • Provide guidance to junior machine learning engineers and the general team (where applicable).
  • Present complex machine learning concepts and results to both technical and non-technical audiences.
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