Activities

• Supports the local CSU in the area of Data Science processes
• Applies data engineering standards and tools to create and maintain data pipelines and extract, transform and archive data
• Automates collecting, cleaning, transforming and archiving datasets, followed by exploratory analysis to identify anomalies
• Selects from existing data sources and prepares data for use by data science models
• Applies existing data science techniques to new problems using specialised programming techniques
• Develops AI solutions to optimize data pipelines and archiving
• Designs, codes, verifies, tests, documents, amends and refactors moderately complex programs/scripts
• Creates data pipeline visualizations and dashboards; evaluates and monitors model performance
• Builds virtual machines, including user, firewalls and resources management

Deliverables

1. Six monthly sprints of AI implementation & support services, with agreed deliverables and acceptance criteria per sprint
2. Delivery Acceptance Sheet (DAS) for each sprint, signed by Contractor and Data Science Team Leader
3. Reports, technology roadmaps and documentation produced per sprint as per acceptance criteria

Coordination and reporting

• Monthly Sprint Planning meetings: define goals, acceptance criteria, backlog review and effort assessment
• Regular sprint execution meetings for progress tracking, continuous feedback, QA/QC and adjustments
• Sprint Review and Retrospective meetings at end of each sprint to assess performance and lessons learned
• Verbal and written sprint outcome reports within three days post-sprint; DAS submission and invoice