We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and their prices. These options typically vary in the start and/or end time of the service requested, such as the date of service or a service time window. The costs and demand can vary widely across these different options, resulting in the need for different prices. We propose a system which allows for segmenting the marketplace (as defined by the particular business) using decision trees, while using parametric discrete choice models within each market segment to accurately estimate conversion behavior. Using parametric discrete choice models allows us to capture important behavioral aspects like reference price effects which naturally occur in scheduled service applications. In addition, we provide natural and fast heuristics to do price optimization. For one such Amazon business where we conducted a live A/B experiment, this new framework outperformed the existing pricing system in every key metric, increasing our target performance metric by 19%, while providing a robust platform to support future new services of the business. The model framework has now been in full production for this business since Q4 2023.