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Job Description
The AI Personalisation Product Lead is the technical product owner for Old Mutual’s AI-driven personalisation engines. This is an engineering-adjacent leadership role that 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. The ideal candidate has a background in machine learning engineering, data science leadership, or AI product management within a production ML environment. They must be fluent in the language of model training, feature engineering, serving infrastructure, latency optimisation, champion-challenger experimentation, and MLOps — not just able to discuss these topics at a conceptual level, but able to make architectural decisions, review model performance, challenge data science approaches, and unblock engineering bottlenecks. This person runs sprint planning, reviews pull requests with the team, triages production incidents, and makes trade-off decisions between model accuracy and serving latency.
The role leads a technical team of 13 to 15 resources in data science, ML and engineering.
Key Responsibilities
Next Best Action (NBA) Engine — Product Ownership
- Own the NBA product vision, technical roadmap, and engineering backlog — defining what capabilities the engine needs to develop, in what sequence, and to what performance standard.
- Define the NBA decision framework: what actions the engine evaluates, what signals it consumes, what constraints it respects (commercial rules, partner obligations, frequency caps, POPIA consent), and how it ranks competing actions.
- Direct the Data Science team on model development.
- Own the real-time serving infrastructure.
- Design and govern champion-challenger experimentation: define experiment protocols, statistical rigour standards, traffic allocation strategies, and decision criteria for model promotion.
- Build and maintain the feature store: curate the real-time and batch feature sets (behavioural, demographic, transactional, contextual) that feed NBA models, ensuring feature freshness, quality, and governance.
- Integrate NBA outputs into Rewards and Digital Platform customer touchpoints: work with Technology/Engineering on 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: ensure action outcomes (accepted, ignored, rejected) are captured, pipeline-processed, and fed back into model training data on a defined cadence.
- Report NBA performance and attributed revenue to Business Owners monthly, with clear methodology transparency.
Content Personalisation Engine — Product Ownership
- 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 (content views, reads, dwell time, shares), stated preferences, and inferred affinities.
- Own the content taxonomy and tagging framework (jointly with Data Architect): define the metadata structure, classification schema, and machine-readable content attributes that enable effective algorithmic matching.
- Integrate personalised content feeds into Digital Platform (app, web, WhatsApp) and Rewards experiences.
- Implement editorial override and curation controls: build mechanisms that allow 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 personalised vs. non-personalised content, content diversity experiments, and filter bubble detection.
- Ensure content diversity to prevent filter bubbles: implement exploration mechanisms, serendipity algorithms, and diversity constraints that maintain content discovery alongside personalisation.
- Monitor content personalisation performance: engagement lift, content consumption depth, return frequency, and personalisation coverage metrics.
Dynamic Optimisation & Experimentation Infrastructure
- Own the experimentation platform: design, build, and maintain the A/B testing and multi-armed bandit infrastructure that enables automated optimisation of campaigns and experiences.
- Implement multi-armed bandit algorithms for automated traffic allocation: where experiments should converge to the winning variant dynamically rather than waiting for manual analysis.
- Build real-time campaign optimisation pipelines: automated bid, creative, and audience adjustment based on streaming performance signals.
- Develop adaptive algorithms for experience personalisation that improve over time through continuous learning — not just static rule-based personalisation.
- Create guardrails and circuit breakers: automated safety mechanisms that prevent optimisation algorithms from degrading experiences, violating business rules, or producing statistically invalid conclusions.
- Establish statistical rigour standards for experiment design: minimum sample sizes, power calculations, duration rules, and significance thresholds.
- Build dashboards that surface experiment results and automated decisions transparently: stakeholders must be able to see what the algorithms are doing and why.
Chatbot AI Personalisation — Product Ownership
- Own the chatbot personalisation product: the AI/ML capability that makes the chatbot contextually aware, brand-consistent, and able to guide users through personalised journeys.
- Integrate the chatbot with NBA engine outputs: the chatbot should be able to surface the NBA-recommended action within conversation, not operate as a disconnected system.
- Integrate the chatbot with the content personalisation engine: the chatbot should recommend relevant content, articles, and features based on the user’s profile and conversation context.
- Build decision trees and personalisation logic for chatbot-guided journeys: the chatbot must be able to navigate users through complex multi-step processes (onboarding, rewards redemption, feature discovery) with contextual personalisation.
- Own chatbot integration into the InteGreat (DAE) adviser platform: define adviser-specific chatbot use cases, conversation flows, and integration requirements.
- Implement chatbot analytics: conversation completion rates, containment rates, personalisation effectiveness (CSAT, conversion from chatbot interactions), and escalation patterns.
- Continuously improve chatbot responses based on conversation log analysis, user feedback, and outcome data.
Customer Micro-Segmentation — Technical Infrastructure
- Own the technical segmentation infrastructure: the models, pipelines, and data systems that produce granular customer segments for precision targeting and personalisation.
- Direct the Data Science team on segmentation model development.
- Build dynamic segments that update in real time as customer behaviour changes — not static segments refreshed weekly.
- Integrate segments into the NBA engine, content personalisation engine, marketing automation platform, and CRM — ensuring segments are actionable across all activation channels.
- Establish segment governance: creation, naming, validation, retirement, and deduplication standards.
- Provide segment insights to the Demand Marketing Leads and Business Owners: segment-level performance reports that prove targeting value and inform campaign strategy.
MLOps, Platform Engineering & Technical Governance
- Own the ML development lifecycle across all AI products: model training, validation, deployment, monitoring, retraining — with defined cadences, quality gates, and rollback procedures.
- Define and enforce model governance standards: model documentation, bias testing, fairness assessment, explainability requirements, and audit trails.
- Own the MLOps pipeline: CI/CD for models, automated testing, deployment orchestration, model versioning, and A/B deployment infrastructure.
- Manage the AI/ML technology stack: ML training platform (MLflow, SageMaker, Vertex AI, or equivalent), model serving infrastructure (KServe, Seldon, or custom), feature store, and experiment platform.
- Lead sprint planning, backlog grooming, and technical prioritisation for the AI Personalisation squad across all four product areas.
- Manage the dedicated engineering resources: Scrum Master, Feature Analysts, Solution Architect, API engineers, and QA engineers — ensuring capacity is allocated effectively across NBA, chatbot, and back-end workstreams.
- Define the multi-frontend integration strategy: how NBA, content personalisation, and chatbot capabilities are exposed across App, Web, WhatsApp, and InteGreat/DAE — not just InteGreat alone.
Required Skills & Experience
- 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.
Skills
Action Planning, Adaptive Thinking, Agile Project Management, Business Requirements Analysis, Commercial Acumen, Computer Literacy, Data Compilation, Data Controls, Executing Plans, IT Network Security, Management Reporting, Negotiation, Policies & Procedures, Project Risk Management, Readiness Assessments, Report Review, Workflow Management
Competencies
Builds Effective Teams
Business Insight
Communicates Effectively
Cultivates Innovation
Decision Quality
Develops Talent
Drives Engagement
Drives Results
Education
NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent
Closing Date
11 July 2026 , 23:59
The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.
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