Knowledge and Experience

Minimum

Knowledge:

• Solution and experimental design for model development
• Machine learning development and underlying theory and assumptions of techniques.
• Predictive modelling techniques (statistical and machine learning) and deployment
• Source control systems e.g. Git, Bitbucket, or SourceTree
• Relational database technologies
• Data Science lifecycle and applicable skills within

Experience:

• If Honours only and not Masters degree then 1-2 years’ experience building machine learning models in Python
• Business analysis and requirements gathering
• Reproducible coding experience and working with source control tools e.g. Git, Bitbucket
• Experience in deploying models into production
• Spark, Hadoop or similar big data coding experience
• Working in remote environments, e.g. Docker, Linux
• Working in cloud environments, e.g. Azure, AWS

Ideal

Knowledge:

• Data analysis
• Solution and experimental design
• Machine learning model architecture (technical design and implementation processes)
• Specialist in one or more specific machine learning competencies, e.g. NLP, Deep Learning etc.
• Understanding of:
  • Underlying theory and application of machine learning models must be able to understand underlying principles and theory and be able to teach others.
  • Best practices for data science
  • Ethical AI design principles
  • Capitec Data Science lifecycle
  • Capitec DPLC

Experience:

• Data science, machine learning and integration of developed solutions.

Skills