Data & Insights COE Lead

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Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

Job Description

The Data & Insights COE Lead owns and operates the analytics Centre of Excellence for the Demand Marketing & AI Personalisation function. This role is the analytical authority that ensures every commercial decision made by Business Owners, every campaign launched by the Demand Marketing team, and every AI product iteration by the Personalisation team is grounded in rigorous, trustworthy data analysis and insight.

Key Responsibilities

COE Hub: Standards, Methodology & Capability Development

• Define and maintain the analytical standards framework: methodology guidelines, statistical rigour requirements, data visualisation standards, insight documentation templates, and quality benchmarks that all analysts (hub and embedded) must follow.
• Own the analytical tooling strategy: select, configure, and govern the BI/visualisation platform (Power BI, Looker, Tableau, or equivalent), advanced analytics environments (Python/R notebooks, SQL IDEs), and self-service query tools.
• Build and maintain a governed semantic/metrics layer that ensures all dashboards, reports, and analyses reference the same trusted, consistently defined metrics — eliminating “duelling dashboards” and metric inconsistencies.
• Define the insight-to-action methodology: a structured framework for how insights are generated, documented, communicated to stakeholders, and tracked for adoption (was the insight acted upon, and what was the result).
• Own professional development for all analysts in the function: structured learning plans, technical skill progression (SQL → Python → statistical modelling → ML), analytical storytelling capability, and career development conversations.
• Conduct regular analytical quality reviews across embedded analysts’ outputs: not approving every deliverable, but running periodic sample audits to ensure methodological rigour, data accuracy, and insight quality are maintained.
• Build and maintain an analytical knowledge base: reusable analysis templates, query libraries, metric definitions, and documented methodologies that accelerate analytical delivery across the function.
• Facilitate cross-channel analytical collaboration: regular analyst community meetings where embedded analysts share learnings, compare approaches, and identify cross-cutting analytical opportunities.

Embedded Analyst Operating Model Management

• Manage the embedded analyst operating rhythm: ensure each embedded analyst (Rewards and Digital) is aligned to their respective product team’s sprint cadence, attends planning sessions, and has clear priorities set by the channel Product Owner.
• Maintain the solid-line/direction-line governance: embedded analysts report to the COE Lead for professional standards, performance reviews, and career development, but receive work direction from their channel Product Owner.
• Manage prioritisation conflicts: when an embedded analyst is overloaded or pulled between competing channel requests, the COE Lead facilitates resolution in collaboration with the channel PO — not by unilaterally redirecting the analyst.
• Ensure cross-functional coverage: the function cannot afford 1:1 analyst-per-PO coverage. Design for analysts covering multiple channel surfaces (e.g., one analyst covering web, app, and WhatsApp for Digital) with clear prioritisation mechanisms when demand exceeds capacity.
• Manage the onboarding of new embedded analysts: ensure they understand both the COE standards and the channel team’s context, operating rhythm, and data landscape.
• Act as the escalation point for embedded analysts: when they encounter data quality issues, infrastructure gaps, or access barriers, the COE Lead’s job is to unblock them — not to add bureaucratic layers.

Embedded Analytics: Rewards Product Area

• Ensure the Rewards embedded team (Data Scientist + Junior Data Analyst) delivers against the Rewards BO’s analytical needs: member acquisition funnel analysis, activation rate tracking, engagement pattern identification, and retention cohort analysis.
• Oversee member value and lifetime value modelling to inform proposition design and investment decisions.
• Ensure redemption pattern analysis, reward attractiveness scoring, and earn-burn dynamics reporting are delivered to the Rewards BO with actionable recommendations.
• Oversee partner performance analytics: transaction volumes, conversion rates, cost per redemption, and partner ROI reporting.
• Ensure the embedded team supports tier and loyalty programme design with data-driven modelling.
• Oversee adviser-specific analytics: adviser adoption dashboards, adviser performance measurement, adviser sentiment tracking, and continuous optimisation through adviser performance analysis.
• Ensure regular insight packs are delivered to the Rewards BO (fortnightly/monthly) with strategic recommendations.
• Ensure the embedded team supports A/B testing and experimentation within the Rewards product.

Embedded Analytics: Digital Platform Product Area

• Ensure the Digital embedded team (Data Scientist + Senior Data Analyst) delivers against the Digital Channels BO’s analytical needs: user journey analysis, navigation pattern mapping, feature adoption tracking, and drop-off/conversion funnel analysis.
• Oversee product analytics instrumentation: event tracking taxonomy, feature flag integration, and tracking plan governance in collaboration with Technology/Engineering.
• Ensure content engagement pattern analysis is delivered to inform content strategy and personalisation decisions.
• Oversee product experiment design, measurement, and interpretation for the Digital Platform team.
• Ensure predictive models for user churn, feature adoption, and engagement trajectory are built and maintained.
• Ensure regular insight packs are delivered to the Digital Channels BO (fortnightly/monthly) with strategic recommendations.
• Ensure platform health monitoring and anomaly alerting (traffic drops, error spikes, performance degradation) is in place.

Self-Service Reporting Ecosystem

• Own the reporting ecosystem: build and maintain the dashboards, data models, and self-service tools that give Business Owners, product managers, and campaign teams real-time visibility into performance.
• Build and maintain executive dashboards for the Rewards BO and Digital Channels BO covering key commercial metrics (members, revenue, engagement, retention, partner performance).
• Develop product-level dashboards for Rewards (member activity, redemption, partner performance, tier progression) and Digital Platform (traffic, registrations, session depth, feature adoption, content engagement).
• Build campaign reporting dashboards with real-time in-flight and post-campaign views, in collaboration with the Campaign Build & Marketing Ops Specialist.
• Implement a self-service analytics layer with governed data models that reduce dependency on manual report requests (target: > 70% self-service vs. manual ratio).
• Train stakeholders on self-service tools and data interpretation: Business Owners and product managers should be able to answer standard questions themselves without filing an analyst request.
• Continuously audit and retire unused reports to keep the reporting estate manageable and the metric layer trustworthy.
• Track dashboard adoption and usage: if dashboards aren’t being used, either the dashboard is wrong or the stakeholder hasn’t been trained — both are this role’s problem to solve.

Campaign Analytics & Attribution

• Own the campaign measurement framework: pre-campaign KPI setting, in-flight monitoring standards, and post-campaign analysis templates.
• Build and maintain multi-touch and algorithmic attribution models that connect campaign activity to commercial outcomes — moving beyond last-click attribution to understand true marketing impact.
• Conduct post-campaign analyses with clear performance decomposition, actionable learnings, and benchmarking against historical campaigns.
• Develop incrementality testing and control group methodologies to prove true campaign lift (not just correlation).
• Build a campaign performance knowledge base that accumulates learnings over time — ensuring the function gets smarter with every campaign rather than repeating mistakes.
• Provide audience and segment performance analytics to inform the Demand Marketing Leads’ targeting decisions.
• Support creative performance analytics: which messages, visuals, and CTAs drive results at the variant level.
• Deliver cross-channel analytics that shows how channels interact and influence each other (halo effects, assisted conversions).
• Report campaign ROI and commercial impact to Business Owners with full transparency on methodology.

Strategic Insights & Executive Narratives

• Establish a regular strategic insight cadence: monthly business reviews with each BO, quarterly deep dives with the executive team, and annual strategic analysis.
• Build analytical narratives that connect data patterns to commercial implications and recommended actions — not just “what happened” but “what it means and what should change.”
• Conduct opportunity sizing and scenario modelling to support strategic decisions: “if we invest X in channel Y, what is the projected return?”
• Develop competitive and market analytics capabilities to contextualise internal performance against the external landscape.
• Proactively identify emerging trends, risks, and opportunities from data before they become obvious — the COE Lead should surface insights that Business Owners haven’t asked for yet.
• Create executive-ready presentations that distil complex analyses into clear, compelling stories that drive action.
• Support Business Owner planning processes with data-driven forecasting and modelling.
• Maintain a strategic insight repository so institutional knowledge is preserved and accessible — not lost when analysts leave.

Required Skills & Experience

• Tertiary qualification in Statistics, Mathematics, Data Science, Economics, Business Analytics, or a related quantitative field. Postgraduate qualification advantageous.
• Minimum 7–10 years of experience in data analytics, business intelligence, or insight roles, with at least 3–5 years in an analytics leadership position managing a team of analysts.
• Proven experience building and operating a COE or hub-and-spoke analytics model — understands the governance dynamics of holding standards centrally while embedding execution in product teams.
• Deep proficiency in SQL and at least one advanced analytics language (Python or R) for statistical analysis, predictive modelling, and data manipulation.
• Strong BI/visualisation expertise: Power BI, Looker, or Tableau at an advanced level, including semantic layer design, data modelling, and governed self-service analytics.
• Experience building and maintaining attribution models (multi-touch, algorithmic, incrementality testing) for marketing and campaign analytics.
• Strong statistical foundation: hypothesis testing, experimental design, regression analysis, cohort analysis, and causal inference — able to review and challenge analytical approaches for methodological rigour.
• Experience with product analytics: user journey analysis, funnel optimisation, feature adoption tracking, and event-based analytics (Amplitude, Mixpanel, or equivalent).
• Excellent analytical storytelling: ability to translate complex data into compelling narratives for executive and Business Owner audiences — not just dashboards, but insight that drives action.
• Experience managing and developing analytical teams: coaching analysts on technical skill progression, analytical storytelling, and career development.
• Strong stakeholder management: ability to operate as a trusted analytical partner to Business Owners, presenting findings transparently and managing competing analytical priorities.
• Experience managing governed data models and metric definitions to ensure consistency across a reporting estate.

Preferred Experience

• Financial services, loyalty programmes, or rewards ecosystem experience.
• Experience embedding analysts into product teams within an Agile/SAFe delivery model.
• Familiarity with data engineering concepts (ETL pipelines, data warehouse architecture) and the ability to collaborate effectively with Data Engineering on data quality and pipeline issues.
• Experience with customer lifetime value modelling, churn prediction, and propensity modelling.
• Experience building analytical capability for both B2C and B2B2C (adviser/intermediary) audiences.
• Exposure to AI/ML product analytics: measuring personalisation lift, NBA acceptance rates, and experiment-driven optimisation.
• Experience with campaign analytics in a multi-channel marketing environment (paid, organic, lifecycle, brand).
• Experience with data governance tools (Collibra, Alation, or equivalent) and data quality frameworks.

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

Directs Work

Drives Engagement

Education

NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent (Required)

Closing Date

11 May 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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