To successfully manage a data science delivery pipeline, you need to:

1. Define clear goals and objectives. What do you want to achieve with your data science project? Once you know what you want to achieve, you can break it down into smaller, more manageable tasks.
2. Establish a cross-functional team. Data science projects often require collaboration between data scientists, engineers, product managers, and other stakeholders. It's important to establish a team that has the skills and expertise to complete all aspects of the project.
3. Choose the right tools and technologies. There are a variety of tools and technologies available to data scientists. Choose the ones that are best suited for your project's needs.
4. Implement a version control system. Version control systems allow you to track changes to your code and data, and to revert to previous versions if necessary.
5. Automate as much as possible. Automating tasks such as data cleaning and feature engineering can save you time and help you to avoid errors.
6. Monitor and evaluate your pipeline. Regularly monitor your pipeline to identify and address any bottlenecks or problems.

Here are some additional tips for successfully managing a data science delivery pipeline:

• Communicate regularly with your team and stakeholders. Keep everyone updated on the progress of the project and any changes to the plan.
• Be flexible and adaptable. Things don't always go according to plan in data science projects. Be prepared to adjust your plans as needed.
• Celebrate successes along the way. It's important to recognize and celebrate the progress that your team is making.

Managing a data science delivery pipeline can be challenging, but it's essential for ensuring the success of your data science projects. By following these tips, you can increase your chances of success.