The AI Personalisation Product Lead is the technical product owner for Old Mutual’s AI-driven personalisation engines.
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.
Accountable for technical maturity, production reliability, and performance improvement of these systems.
Runs sprint planning, reviews pull requests with the team, triages production incidents, and makes trade-off decisions between model accuracy and serving latency.
Leads a technical team of 13 (4 permanent data science/ML roles + 9 dedicated engineering resources).
Requirements
Tertiary qualification in Computer Science, Machine Learning, Data Science, Statistics, Engineering, or a related technical field.
Postgraduate (MSc/PhD) in ML, AI, or statistical modelling strongly preferred.
Minimum 7–10 years of experience in ML/AI product development, data science, or ML engineering, with at least 3–5 years in a product lead, tech lead, or senior data science leadership role.
Deep technical fluency in machine learning: supervised and unsupervised learning, recommendation systems, NLP/NLU, reinforcement learning, multi-armed bandits, and real-time model serving.
Must be able to review model architectures, challenge experimental designs, and make build-vs-buy technical decisions.
Production ML experience: has built and operated ML systems that serve real-time decisions at scale (not just research/experimentation).
Understands latency optimisation, feature store design, model drift, and serving infrastructure.
Hands-on proficiency (current or recent) in Python, SQL, and at least one ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost).
Not expected to write production code daily, but must be able to read code, review PRs, and prototype approaches.
Experience with MLOps practices: CI/CD for models, experiment tracking (MLflow, Weights & Biases), model monitoring, automated retraining pipelines, and deployment orchestration.
Experience with real-time serving infrastructure: low-latency API design, feature serving, and integration with consumer-facing digital products.
Experience leading cross-functional technical teams: data scientists, ML engineers, software engineers, QA, and solution architects working together on AI product delivery.
Product management skills: ability to define product vision, manage a technical backlog, make prioritisation trade-offs, and communicate technical progress to non-technical Business Owners.
Experience with experimentation and A/B testing at scale: champion-challenger design, statistical rigour, and multi-armed bandit deployment.
Strong communication skills: ability to translate complex ML concepts into business impact language for executive and Business Owner audiences, and to write clear technical documentation for engineering teams.