Job Description
- We are looking for talented data scientist individuals to support with our logistics international team.
- Will support within the platform space – assist in setting up best practices, guiding principles, governance templates as well as monitoring and operations support.
- Data management supporting specialists who will be involved in standardisation; Data ingestion topics; Kafka.
Outputs:
- Ensure to interpret business strategy and determine innovative solutions supporting strategy implementation.
- Data Architecture design, modelling of data storage / data flow in data warehouses and operative systems. Manage data and meta-data.
- Define the standards for data modelling and the maintenance thereof.
- Support and maintain machine learning algorithm deployments. Build ETL (data extraction, transformation and loading) storages and analytical tools.
- Manage and evaluate innovation pipeline assisting strategic decision-making and Go-to-Market process.
- Design, research, develop, analyse and suggest new concepts as well as strategies.
- Lead and direct different cross-functional programs and projects of varied sizes and types concurrently.
- Manage and attain customer expectations and ensure delivery to fulfil expectations.
- Coordinate all cross-functional activities, represent customers internally and collaborate with leaders in varied areas on allocation across projects and resource planning.
- Ensure to exercise decision-making and independent judgment while being aligned with customers and management.
- Promote innovations to support business requirements through activities that test, pilot and implement innovative concepts
- Roll out and analyse the effectiveness of initiatives that leverage multiplatform technologies
Job Requirements:
- Minimum NQF 7 – BSC/BCom/BTech in Information Technology or B. Eng. (Industrial Engineering)
- Minimum 3+ Years’ experience in IT
- Expert with ETL tools and BI Tools
- Data Science / Data Engineering
- Big data technologies: Hadoop
- Network/Security/Credential management
- Middleware Integration
- Data Ingestion
- Kafka experience and Knowledge
- API orientated approach and knowledge