AI Personalisation Product Lead

Job Summary

The AI Personalisation Product Lead is the technical product owner for Old Mutual’s AI‑driven personalisation engines. This engineering‑adjacent leadership role owns the product track for four interconnected AI systems: the Next Best Action (NBA) engine, the content personalisation engine, the dynamic optimisation infrastructure, and the chatbot personalisation capability. The Lead is accountable for the technical maturity, production reliability, and performance improvement of these systems – not for the commercial proposition they serve.

This is a deeply technical role requiring strong machine learning engineering, data science leadership, or AI product management experience within a production ML environment.

• Own the NBA product vision, technical roadmap, and engineering backlog – defining capabilities, sequence of development, and performance standards.
• Define the NBA decision framework: actions evaluated, signals consumed, constraints respected (commercial rules, partner obligations, frequency caps, POPIA consent), and ranking of competing actions.
• Direct the Data Science team on model development.
• Own the real‑time serving infrastructure.
• Design and govern champion‑challenger experimentation: experiment protocols, statistical rigour standards, traffic allocation strategies, and decision criteria for model promotion.
• Build and maintain the feature store: curate real‑time and batch feature sets (behavioural, demographic, transactional, contextual) that feed NBA models, ensuring freshness, quality, and governance.
• Integrate NBA outputs into Rewards and Digital Platform customer touchpoints: API design, integration patterns, fallback handling, and error management.
• Monitor NBA performance daily: model accuracy (AUC/precision/recall), acceptance rates, revenue attribution, latency metrics, and drift detection, triggering retraining when performance degrades.
• Build feedback loops to capture action outcomes and feed back into model training data on a defined cadence.
• Report NBA performance and attributed revenue to Business Owners monthly with transparent methodology.

• Own the content personalisation product vision and technical strategy: how the engine determines which content, articles, offers, and recommendations to serve to each user.
• Direct the development of content recommendation algorithms: collaborative filtering, content‑based filtering, hybrid approaches, and contextual re‑ranking models.
• Build and maintain user interest profiles from behavioural data, stated preferences, and inferred affinities.
• Own the content taxonomy and tagging framework in partnership with the Data Architect.
• Integrate personalised content feeds into Digital Platform (app, web, Whats App) and Rewards experiences.
• Implement editorial override and curation controls for marketing and content teams to pin, boost, suppress, or schedule specific content alongside algorithmic recommendations.
• Design and run experiments to measure personalisation impact: A/B tests of personalized vs. non‑personalized content, content diversity experiments, and filter bubble detection.
• Ensure content diversity to prevent filter bubbles by implementing exploration mechanisms, serendipity algorithms, and diversity constraints.
• Monitor content personalisation performance: engagement lift, content consumption depth, return frequency, and personalisation coverage metrics.

• Own the experimentation platform: design, build, and maintain the A/B testing and multi‑armed bandit infrastructure enabling automated optimisation of campaigns and experiences.
• Implement multi‑armed bandit algorithms for automated traffic allocation.
• Build real‑time campaign optimisation pipelines for automated bid, creative, and audience adjustment based on streaming performance signals.
• Develop adaptive algorithms for experience personalisation that improve over time through continuous learning.
• Create guardrails and circuit breakers to prevent optimisation algorithms from degrading experiences or violating…