Qualifications and experience:
- 4 to 6 years Machine Learning and Software Engineering experience
- Strong analytical and problem-solving skills
- Expert in Python and SQL
- Experience with modern software development best practices, e.g.
- Agile software development
- Code reviews
- Unit testing
- Version control, e.g. git
- CI/CD
- Experience with microservice architectures
- Experience working in an agile team
- Experience with ML frameworks and tools (e.g. pandas, NumPy, scikit-learn, TensorFlow, Pytorch, Spark MLlib)
- Experience with modern ETL, compute and orchestration frameworks, e.g. Apache Spark, Apache Flink, Apache Kafka, etc.
- Development experience in both Windows and Linux
- Experience with container technologies, e.g. Docker, Kubernetes
- Experience in building machine learning or AI systems
- Proficiency in R language
- Experience deploying models to production
- Experience building distributed systems
- Experience with NoSQL databases
- Experience working with ML platforms, e.g. MLflow, Kubeflow, etc.
- Experience working with Data Science platforms, e.g. Dataiku, Domino, etc.
- Experience with cloud-based infrastructure, e.g. Azure, AWS, GCP; (AWS preferred)
- A relevant degree in Information Technology - Computer Science or Engineering Other
- A relevant degree in Information Technology - Computer Science or Engineering - Other
Knowledge of:
- Object oriented and functional programming in Python
- Modern software development practices
- Database querying using SQL
- Data science life cycle
- Machine learning concepts
- Machine learning model life cycle
- Microservice architectures
Ideal:
- Data Science lifecycle
- Distributed system design
- Big data storage and processing solutions
- Machine learning model architectures
Skills:
- Analytical Skills
- Decision making skills
- Planning, organising and coordination skills
- Problem solving skills
- Researching skills
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