Building a career with Datonomy allows you to work on projects that interest you, and with the tech stack that appeals to you most. Diverse teams comprising a variety of cultures, ages and backgrounds are proven to be more effective - this also ensures that teams don''t become rigid and change-averse.
Datonomy invites you to embrace the future of work. Consulting gives you the flexibility to co-create your career with clients who rely on your unique skillset. The beauty of the model is that you can choose the length of your engagement with each client - you may want to spend a year or two rolling out a major programme, or just a few months designing a product feature.
At Datonomy, we want to collaborate with you to achieve your goals, personally and professionally, and that is why we want like-minded people to join our growing team.
The Machine Learning Engineer is primarily responsible for building end-to-end machine learning models from ideation to deployment and scalability. S/he creates 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.
- 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, autoencoders, 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 TensorFlow.
- 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, 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 general team (where applicable).
- Present complex machine learning concepts and results to both technical and non-technical audiences.
4 years experience / NQF 7