# White Paper on Data Science Technical Program Management

DevFeed: [White Paper on Data Science Technical Program Management](<https://devfeed.tech/articles/white-paper-on-data-science-technical-program-management-22548.md>)

Original publisher: [Read original article](<https://medium.com/walmartglobaltech/white-paper-on-data-science-technical-program-management-08dc2535bd1a?source=rss----905ea2b3d4d1---4>)

Author: Sonu Jain

Published: 2026-02-27T12:41:46Z

Content type: article

Language: en

Sources: [Walmart Global Tech](<https://devfeed.tech/sources/walmart-global-tech.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [Development](<https://devfeed.tech/topics/development.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>)

Tags: [collaboration](<https://devfeed.tech/tags/collaboration.md>), [coverage](<https://devfeed.tech/tags/coverage.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [experimental](<https://devfeed.tech/tags/experimental.md>), [leadership](<https://devfeed.tech/tags/leadership.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [management](<https://devfeed.tech/tags/management.md>), [paper](<https://devfeed.tech/tags/paper.md>), [retail](<https://devfeed.tech/tags/retail.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [technical](<https://devfeed.tech/tags/technical.md>), [technical-program-manager](<https://devfeed.tech/tags/technical-program-manager.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>), [white-paper](<https://devfeed.tech/tags/white-paper.md>)

## AI overview

This white paper presents a structured approach to managing Data Science programs through technical program management. It discusses business alignment, cross-functional collaboration, data validation, model training and retraining, governance, and phased execution, using an inventory forecasting initiative as a real-world example.

## Source excerpt

1. Abstract Managing Data Science programs requires a structured approach to handle the complexities of data, model development, and business alignment. This whitepaper provides a comprehensive guide on the effective program management of Data Science programs by technical program managers. It highlights the critical role of Technical Program Managers (TPMs) in driving successful execution and outlines the key phases, challenges, and recommended best practices at every stage for effectively managing Data Science programs This white paper is grounded in a real-world inventory forecasting initiative aimed at improving stock availability and reducing overstock across multiple retail categories. The program involved cross-functional collaboration between Data Science, Engineering, Product, and Business teams to build predictive models that could dynamically adjust inventory levels based on demand signals. 2. Introduction Data Science has become a critical pillar of decision-making across industries, but organizations continue to struggle with operationalizing these initiatives. Unlike software development, which follows predictable sprint cycles, Data Science programs are inherently experimental -- requiring repeated cycles of data validation, model training, and retraining before they reach acceptable performance levels. This uncertainty often leads to misaligned expectations, delays in delivery, and inconsistent business impact. The iterative nature of model development makes predictability especially challenging: teams may require multiple iterations to achieve coverage and accuracy thresholds that satisfy business needs. Without structured program management, these efforts risk becoming siloed experiments rather than scalable, value-generating solutions. This whitepaper aims to address this gap by providing a practical framework for Technical Program Managers (TPMs) to manage Data Science programs effectively. It draws on real-world experience from a large-scale inve