SCOPE OF WORK / DUTIES / ROLES

• Applies existing machine learning techniques to new problems and datasets;
• Evaluates the outcomes and performance of machine learning systems;
• Identifies issues and recommends improvements to machine learning systems and the data they use.

Programming:

• Designs, codes, verifies, tests, documents, amends and refactors moderately complex programs/scripts;
• Applies agreed standards and tools to achieve a well-engineered result;
• Monitors and reports on progress;
• Identifies issues related to ML development activities;
• Proposes practical ML solutions to resolve issues;
• Collaborates in reviews of work with others as appropriate. Systems and software / ML life cycle engineering;
• Elicits requirements for systems and software / ML life cycle working practices and automation;
• Selects systems and software / ML life cycle working practices for components and micro-services;
• Deploys automation to achieve well-engineered and secure outcomes.

Systems integration and build:

• Defines the ML modules needed for an integration build and produces a build definition for each generation of the solution;
• Accepts completed ML modules, ensuring that they meet defined criteria.

Data science:

• Applies existing data science techniques to new problems and datasets using specialised programming techniques;
• Evaluates the outcomes and performance of data science models;
• Identifies and implements opportunities to train and improve models and the data they use.

Data engineering:

• Applies data engineering standards and tools to create and maintain data pipelines and extract, transform and load data.

Emerging technology monitoring:

• Supports monitoring of the external environment and assessment of emerging technologies;
• Contributes to the creation of reports, technology road mapping and the sharing of knowledge and insights.