AI Personalisation Product Lead

Role Overview

• 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.

Tech Stack

• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health insurance
• retirement plans
• paid time off
• flexible work arrangements
• professional development