We present ENCODE, an innovative ensemble-based Contextual Bandit (CB) model, engineered explicitly for dynamic pricing in large-scale e-commerce for kid’s clothing. ENCODE uniquely addresses the retailer’s multifaceted need for optimal pricing – both immediate and cumulative – subject to market-driven price triggers like competitor fluctuations and seasonal trends. The model integrates four cornerstone CB algorithms: LinUCB, Vowpal Wabbit, Contextual Thompson Sampling, and BayesUCB, delivering a unified solution for maximizing yield in alignment with business constraints.Our model’s originality manifests in its comprehensive treatment of complex, real-world pricing issues: from optimizing long-tail products with limited data to seamlessly managing an extensive array of products and catering to diverse customer segments. ENCODE also uniquely accommodates pricing strategies within product families, is highly responsive to shifts in competitor pricing, and accounts for the intricate interplay between related products in a non-stationary environment.Demonstrably effective, the model yielded a 13.8% improvement in margin through Contextual Thompson Sampling alone, with an additional 6% gain from considering inter-related products. In comparative analysis, ENCODE surpassed standalone CB algorithms by 19% in cumulative margins. It also boasts computational efficiency, optimized through dimensionality reduction and hyperparameter fine-tuning. Scalability is ensured through advanced cloud-based implementations. Hence, ENCODE stands as a comprehensive, highly scalable, and markedly effective solution to contemporary e-commerce pricing complexities.