Customer Value Science & Offer Optimization (Data Scientist)

Job Purpose :
To maximise the recharge value and revenue recovery of churned and disengaged subscribers by developing AI-driven win-back
science, propensity modelling, and agentic offer optimisation systems. This role delivers commercially actionable intelligence on
re-engagement offer design, recharge stimulation, and ARPU restoration — ensuring that lapsed subscribers are identified, prioritised,
and converted back into active, revenue-generating users at every stage of the engagement and graduation lifecycle.

Main Responsibilities :
1. Develop and maintain propensity models, segmentation algorithms, and predictive scoring systems to identify customer behaviour
patterns, conversion likelihood, and intervention opportunities across assigned lifecycle stages.
2. Build and implement CLM monetization models that identify optimal offer timing, channel, and pricing for recharge stimulation and
ARPU recovery within the assigned customer lifecycle stage.
3. Contribute to automated decisioning pipelines that enable real-time, AI-driven customer interventions, and report model performance
and business impact metrics in regular KPI reviews.
4. Build win-back propensity models, lapsed-user clustering, and multi-task models to predict re-engagement likelihood and optimal
offer response rates for churned subscribers.
5. Design and implement offer optimisation frameworks (e.g., multi-armed bandits) and feature engineering pipelines that personalise
re-engagement interventions and maximise revenue recovery across the engagement and graduation lifecycle stages.