Head: Decision Intelligence at Santam Insurance

PURPOSE OF THE ROLE

• Traditional insurers make product decisions. Digital businesses make customer decisions. NMSIS must do both. Every day we determine which customers should receive which offer, which products create the greatest long-term value, which channels should engage them, how to prevent lapses, optimise pricing, improve claims outcomes and maximise profitability.
• The Head: Decision Intelligence will build the capability that turns data into commercial decisions — improving growth, customer outcomes and profitability across the business. As NMSIS evolves into a platform orchestrator within the MultiChoice ecosystem, this role is being created to give the business dedicated, accountable ownership of enterprise-wide decision intelligence, lead optimisation, predictive analytics and customer lifetime value management.
• This is not a reporting role, a traditional analytics role or a pure data science role. It sits where business strategy, insurance, customer behaviour, artificial intelligence and advanced analytics intersect. You will build the commercial operating system powering Insurance, Customer Intelligence, Lead Optimisation, Pricing Feedback Loops, Behavioural Decisioning, Next Best Action, Customer Lifetime Value Optimisation, Growth Intelligence, Portfolio Performance and Executive Decision Support — acting as the central integration point between NMSIS, MultiChoice CII, Sanlam BI & Data Engineering, Santam and TrueSouth Actuaries

QUALIFICATIONS

• Degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Actuarial Science or a related quantitative discipline (essential).
• Postgraduate qualification in a quantitative or technical discipline (advantageous).

EXPERIENCE REQUIRED

• 5–8 years' experience in analytics, data science or data engineering within a commercial or financial services environment.
• Hands-on proficiency in SQL and Python (or R), with demonstrated experience designing and deploying propensity or segmentation models in a production environment.
• Experience governing multi-partner data delivery relationships, including SLA management, data quality standards and governance frameworks.
• A track record of translating complex analytical outputs into commercially actionable decisions and senior-level intelligence reporting.
• Ability to operate fluently across both technical teams (data engineers, data scientists) and business stakeholders (EXCO, distribution, operations) simultaneously.
• Experience within financial services, insurance, banking, telecommunications, retail, digital platforms, consulting or customer intelligence; experience leading multidisciplinary business and technology teams is advantageous.

Advantageous:

• Insurance or embedded financial services experience, with an understanding of underwriting, pricing and portfolio economics.
• Experience with Snowflake, Databricks, Power BI or equivalent cloud-based analytics and data warehousing platforms.
• Exposure to decisioning platforms, rules engines or campaign orchestration tooling.
• Familiarity with insurance economics: VNB, CLV, persistency, CAC payback and contribution margin.
• POPIA, FAIS or equivalent regulatory data compliance experience in a South African context.

DUTIES AND RESPONSIBILITIES
Key Responsibilities:

Data Foundation & Engineering

• Own the integrated data model across all relevant domains — DStv Subscriber ID, Insurance Policy ID, CRM Customer Number, Payments Account ID, and Identity Number — linked via a unified customer identity layer that connects subscriber, lead, policy, claims, billing, rewards and behavioural signals.
• Govern MultiChoice/CII data delivery: SLA standards, field definitions, delivery frequency, quality thresholds and escalation paths, driving the roadmap from monthly batch delivery toward event-triggered data flows as infrastructure matures.
• Lead data engineering into Sanlam: governed, versioned, lineage-tracked analytical datasets that are fit for decisioning, not only fit for reporting.
• Maintain a structured data gap register with commercial impact quantification, identifying precisely where deficiencies are constraining model quality, automation or commercial optimisation.
• Ensure POPIA compliance and all data sharing agreement obligations with MultiChoice are embedded in every data flow and processing activity.

Models & Advanced Analytics

• Direct and govern the full model suite in partnership with CII, Sanlam Data Science and TrueSouth, including: Lead Quality Index (LQI) — multi-factor scoring incorporating economic proxies, lapse risk, claims probability and behavioural signals, moving beyond contactability-only scoring; cross-sell propensity models — product-specific acquisition propensity per product house (e.g. Entertainment Plus, Funeral Plus, Life Plus, Home, Home Contents, Device upgrade loans), enriched with LQI to prevent volume-over-quality lead acceptance; NTU prediction — identifying pre-sale signals that predict non-conversion, enabling intervention before commitment; lapse and persistency models — early duration (M1–M3) lapse prediction, affordability vulnerability scoring and behaviour-based retention triggers; CLV and CAC optimisation — multi-product customer lifetime value accounting for cross-sell probability, claims experience, reward cost and contribution margin, with CAC payback modelling by channel and segment; and claims risk and pricing feedback — frequency and severity prediction, fraud signal detection, repair-vs-replace optimisation, and variance monitoring against pricing assumptions.
• Govern model performance standards: documentation, drift monitoring, recalibration triggers, champion/challenger frameworks and model risk governance aligned to Sanlam Group standards.
• Ensure all models are grounded in insurance economics — VNB, persistency, contribution margin, capital efficiency — and remain aligned to customer fairness and FSCA conduct expectations.

DUTIES AND RESPONSIBILITIES
Decisioning & Lead Intelligence

• Design and operationalise the lead segmentation and routing framework: channelling each lead to the right product house, sales channel and offer configuration based on LQI score, propensity, channel economics and cross-sell potential — not raw contactability or volume.
• Ensure lead allocation logic balances conversion probability, persistency probability, product margin, channel economics (CAC by channel) and cross-sell potential simultaneously, not any single metric in isolation.
• Build closed-loop feedback between lead outcomes (conversion, early lapse, NTU, claims) and upstream scoring, enabling continuous improvement in lead quality without requiring manual recalibration cycles.
• Design structured decisioning frameworks for acquisition, retention and cross-sell, translating model outputs into executable business rules, scoring logic and decision playbooks that BPO teams, campaign managers and distribution leads can act on directly.
• Drive the evolution from human-led toward rules-driven, model-assisted and AI-augmented execution, enabling the organisation to operate at scale despite constrained headcount.
• Support scenario and trade-off analysis: conversion vs persistency; CAC vs CLV thresholds; channel investment allocation; pricing adequacy vs growth rate.

Execution, Governance & Commercial Intelligence

• Integrate model outputs into campaign targeting systems, BPO dialling and scripting logic, digital campaign tools and retention workflow engines, working in close partnership with the CGO and COO teams.
• Produce EXCO-grade commercial intelligence: CLV by segment, CAC payback by channel and product, channel economics scorecards, lapse cohort analysis, product contribution margin reports and VNB attribution by segment and channel.
• Establish and govern a test-and-learn architecture: control group methodology, A/B testing for decisioning variants, campaign uplift measurement and decision performance tracking against defined OKRs and enterprise scorecard metrics.
• Own DI decision governance, ensuring all production models and decisioning rules are transparent, explainable, documented and aligned to regulatory, conduct and risk appetite guardrails; identify and escalate where decisions are destroying economic value or weakening capital efficiency.
• Chair the NMSIS/MultiChoice (CII) DI operating rhythm, covering data foundation, engineering, model and advanced analytics, decisioning and lead intelligence, model performance reviews, data quality reviews and use case backlog management.
• Own the DI maturity roadmap — a structured, phased progression from the current Level 2 (Siloed) toward Level 4 (Ecosystem Intelligence) across data foundation, integration, product reporting, growth analytics and AI-assisted decisioning.
• Maintain reusable decision assets that are version-controlled and actively used — documented rules, scoring logic, prioritisation frameworks and performance feedback loops — building decisioning as a repeatable organisational capability, not an individual dependency.

WHAT YOU'LL NEED

• Strong commercial acumen and analytical thinking.
• Customer decisioning expertise and executive communication skills.
• Stakeholder management capability across technical and business audiences.
• Intellectual curiosity, technical confidence and a structured, outcomes-driven approach to problem-solving.
• A passion for building new capabilities from the ground up.