Strategic Intelligence & Algorithmic Governance

Job Purpose

• Own the end-to-end science practice for customer retention, graduation, and revenue growth as the most senior individual
• contributor in Customer Science. This role combines deep technical execution — advanced ML modelling, causal inference,
• agentic AI deployment, and graph-based network intelligence — with strategic influence: translating data science output into
• commercial decisions, governing algorithmic quality across all squads, and setting the analytical gold standard for XL Smart's
• subscriber base and ARPU improvement initiatives.

Main Responsibilities

1. Design and own end-to-end ML models for churn prediction, revenue graduation, and lifetime value from feature engineering through production deployment and ongoing monitoring.
2. Lead causal inference and A/B experimentation frameworks; establish and enforce statistical rigour across all squad experiments and commercial interventions.
3. Build and maintain advanced strategic intelligence models: market dynamics, competitive signal detection, pricing elasticity, and network quality impact on churn.
4. Architect and deploy agentic AI systems autonomous real-time ML scoring, bandit-based optimisation, and self-learning decisioning pipelines.
5. Own the feature store and model registry: ensure quality, reproducibility, versioning, and governance of all Customer Science outputs.
6. Define and enforce model deployment standards, drift monitoring, and retraining protocols across all squads.
7. Translate model outputs into commercial briefs for senior stakeholders (Strategy, Analytics, Segment); serve as the scientific authority on all outbound Customer Science findings.
8. Lead monthly data discovery sprints to identify and resolve upstream data gaps; present findings at weekly Group Head reviews.
9. Drive the 30% platform migration workstream (MLOps, data engineering, cloud ML infrastructure) alongside 70% commercial task-force deliverables reviewed biweekly.
10. Mentor squad analysts in advanced data science methods: causal inference, graph ML, reinforcement learning, and LLM-assisted analytics.

Requirements

• S1 (Strata 1) in Computer Science, Statistics, Data Science, Mathematics, or Engineering. S2/Master's degree in a quantitative field preferred.
• Minimum 7–10 years of hands-on experience in Data Science, Customer Analytics, or AI/ML roles; including at least 3 years as a principal scientist or lead practitioner owning production ML systems.
• Certified Data Scientist (CDS), Google Professional Machine Learning Engineer, AWS Certified Machine Learning — Specialty, or equivalent advanced ML certification. PMP or equivalent project governance credential is a plus.