Analytics & AI Enablement Specialist

Job Advert Summary

To design, build, operationalise and support trusted analytics and AI-enabled solutions that improve business decisions, member outcomes, commercial performance and operational efficiency across the PPS Group. The role will translate defined business opportunities into robust data assets, analytical models, semantic models, dashboards and governed AI workflows. Working under the direction of the Lead, the incumbent will combine strong technical delivery with business understanding, documentation, quality assurance and stakeholder enablement.

Minimum Requirements

Education:

• Bachelor's degree in Statistics, Mathematics, Data Science, Computer Science, Information Systems, Engineering, Actuarial Science or a related quantitative field.
• Postgraduate qualification or relevant industry certifications (preferred).

Experience:

• Approximately 3 to 6 years of relevant experience in analytics, business intelligence, data engineering, data science or AI solution delivery.
• Experience in financial services, insurance, membership organisations or another regulated environment (preferred).
• Demonstrated experience taking analytical work from ambiguous questions through data preparation, validation, insight and usable output.

Knowledge and Skills:

• Advanced SQL, including data transformation, reconciliation, performance-conscious querying and dimensional modelling.
• Practical Python for data preparation, automation, analytics and model development using maintainable, version-controlled code.
• Strong Power BI capability covering data modelling, DAX, report design, performance, row-level security and deployment considerations.
• Working knowledge of Microsoft Fabric, lakehouse and warehouse concepts, Delta tables, pipelines, semantic models and cloud analytics patterns.
• Understanding of statistical analysis, model evaluation, segmentation, forecasting and responsible interpretation of results.
• Practical understanding of generative AI, retrieval, embeddings, prompt design, grounding, evaluation, guardrails and human oversight.
• Knowledge of data governance, metadata, lineage, access control, privacy, testing and auditability.

Duties and Responsibilities

• Convert prioritised business questions and use cases into clear analytical requirements, delivery plans and measurable outputs.
• Build end-to-end analytical solutions using SQL, Python, Power BI, Microsoft Fabric and other approved technologies.
• Perform exploratory, diagnostic, predictive and prescriptive analysis where appropriate, with emphasis on practical business action.
• Communicate progress, risks, dependencies and outcomes clearly to the Lead and relevant stakeholders.
• Develop and maintain curated datasets, gold-layer tables, feature sets, semantic models and reusable analytical outputs.
• Integrate internal and approved external data sources to create consistent member, market, product, distribution and operational views.
• Apply sound dimensional modelling, metadata, business definitions, lineage and version-control practices.
• Design solutions for reuse and scale rather than repeated spreadsheet extracts or isolated analyses.
• Design intuitive Power BI reports and decision-support tools with clear metrics, drill paths and executive-ready narratives.
• Develop measures, calculations and analytical views that explain performance, conversion, lifecycle movement, opportunity and value.
• Test report security, semantic-model access, refresh behaviour and visual accuracy before release.
• Analyse member, graduate, advisor, broker, product and market behaviour to identify growth, retention and efficiency opportunities.
• Develop segmentation, propensity, forecasting, classification or other analytical models when justified by the business problem.
• Evaluate model and insight quality against business relevance, not technical performance alone.
• Build and test approved AI-enabled workflows, including retrieval-augmented generation, structured recommendations and human-review processes.
• Implement controls for access, grounding, traceability, prompt and output management, auditability and responsible use.
• Prototype practical AI use cases and support their transition from demonstration into controlled, supportable solutions.
• Document limitations, validation results, decision rules and escalation points, and never present unverified AI output as fact.
• Profile source data, identify anomalies and reconcile outputs to trusted sources and agreed business rules.
• Create repeatable validation checks for completeness, accuracy, timeliness, security and consistency.
• Support scheduled refreshes, production incidents and root-cause analysis across owned solutions.
• Maintain technical documentation, user guidance, change records and handover material.
• Present findings in clear business language with defensible evidence, implications and recommended actions.
• Contribute reusable code, templates, standards and knowledge that strengthen the wider analytics capability.