• Design and implement large-scale decision engines that dynamically resolve customer identity and intent in real time. • Develop optimization frameworks (multi-armed bandits, reinforcement learning, constrained optimization) to balance personalization, business KPIs, and operational constraints. • Build session-based and sequence-aware models (transformers, RNNs, temporal point processes) to capture evolving customer intent. • Incorporate multimodal signals (clickstream, search queries, transaction history, CRM attributes) into unified intent prediction pipelines. • Deploy models with low-latency inference for real-time decisioning at scale. • Define rigorous evaluation metrics (precision/recall for identity resolution, uplift for causal models, latency for decision engines). • Lead online experimentation (A/B tests, multi-cell experiments) to validate model impact on personalization, conversion, and customer experience. • Apply counterfactual evaluation techniques to measure causal impact of identity decisions. • Translate technical insights into clear recommendations for product, marketing, and executive stakeholders. • Partner with product managers to align decision engine outputs with business objectives (e.g., personalization, fraud prevention, customer support). • Ensure models are production-ready with robust monitoring, retraining schedules, and A/B testing frameworks. • Implement feature stores and model registries to streamline experimentation and deployment.