Strategic Intelligence & Algorithmic Governance

Description 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

• 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.
• Lead causal inference and A/B experimentation frameworks; establish and enforce statistical rigour across all squad experiments and commercial interventions.
• Build and maintain advanced strategic intelligence models: market dynamics, competitive signal detection, pricing elasticity, and network quality impact on churn.
• Architect and deploy agentic AI systems autonomous real-time ML scoring, bandit-based optimisation, and self-learning decisioning pipelines.
• Own the feature store and model registry: ensure quality, reproducibility, versioning, and governance of all Customer Science outputs.
• Define and enforce model deployment standards, drift monitoring, and retraining protocols across all squads.
• Translate model outputs into commercial briefs for senior stakeholders (Strategy, Analytics, Segment); serve as the scientific authority on all outbound Customer Science findings.
• Lead monthly data discovery sprints to identify and resolve upstream data gaps; present findings at weekly Group Head reviews.
• Drive the 30% platform migration workstream (MLOps, data engineering, cloud ML infrastructure) alongside 70% commercial task-force deliverables reviewed biweekly.
• 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.

Employment Status: Permanent