Oversee data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions. Oversee predictive modelling.

Minimum Qualifications
Type of Qualification: Post Graduate Degree
Field of Study: Information Technology
Type of Qualification: Post Graduate Diploma
Field of Study: Information Studies

Experience Required

• Utilise technical knowledge of, and experience in, the following to address AI and ML solution requirements - ML Concepts; Software Engineering Discipline (e.g. Source Control, CI/CD); major ML frameworks (Tensorflow, PyTorch, scikit-learn); Big Data Processing Libraries (e.g. Spark, Dask); structured and unstructured data; building end-to-end solutions on Cloud (Azure); working with and tuning pre-built AI Services in Cloud; Data Visualisation software (Power BI etc.).
• Deliver technical artefacts articulating the architecture and model robustness of AI solutions, supporting the development of Technical Documentation within the area.
• Develop web applications using Python frameworks to enable business to interact with Machine Learning models.
• Provide automation support for ML pipelines; build code, run tests (CI), and safely deploy a new version of an application (CD) to allow for the removal of manual errors, and provide standardised feedback loops, to enable fast product iterations.
• Apply new techniques and technologies to business problems and solve them in new and creative ways in order to provide greater insight, accuracy and consistency.
• Investigate and implement latest large language models to mature the Virtual risk manager (Chatbots): enable the virtual risk manager to be used as a channel to deliver risk solutions.
• Work with business stakeholders to identify new AI initiatives. Perform rapid EDA / prototyping exercises to help size projects and high-level success criteria, meeting the project pipelining requirements of the role.
• Work with internal clients to shape new AI projects, meeting client needs as identified.
• Present relevant content at architecture, technical committees and business stakeholders
• Produce Solution Designs and Architecture for review / vetting, and reusable solution patterns for use in other projects, supporting the enablement of other team members.
• Discuss needs with the user and collaborate with the wider team to contribute to the development of a solution that is user-centric and aligned to business goals, ensuring that the solution is fit-for-purpose.
• Conduct research into and carry out AI and ML development processes to ensure the business unit fulfils the needs of AI and ML strategies.
• Convert conceptual AI needs (non-technical) into crystalized problem statements that can be scientifically measured to support these needs.
• Apply engineering and math skills to analyse and prepare structured/unstructured data for modelling, to support the Data Analysis and Engineering requirements of the role.
• Apply the most appropriate algorithms and/or build novel algorithms/techniques to fit the problem statement identified, which will support AI Modelling; take advantage of pre-built AI capabilities offered by Cloud vendors where appropriate to increase project velocity, take advantage of latest start-of-the-art commercialized AI solutions and reduce internal technical debt.
• Incorporate AI models into software that meets the upstream or downstream system requirements (i.e., Engineering Batch, Streaming or API based ML pipelines), supporting the solutioning of the team.

Behavioral Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Details
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

• Data Analysis
• Data Integrity
• Database Administration
• Knowledge Classification
• Research & Information Gathering