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Project lead tasks in the Team Data Science Process

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This article describes tasks that a project lead completes setting up a repository for their project team in the Team Data Science Process (TDSP). The TDSP is a framework developed by Microsoft that provides a structured sequence of activities to efficiently execute cloud-based, predictive analytics solutions. The TDSP is designed to help improve collaboration and team learning. For an outline of the personnel roles and associated tasks, see Team Data Science Process roles and tasks.

A project lead manages the daily activities of individual data scientists on a specific data science project in the TDSP.

Major roles of the project lead

  • Project planning and execution:
    • Develop and execute detailed project plans, including defining project scope, timelines, milestones, and deliverables.
    • Coordinate and oversee all project activities, ensuring adherence to the project plan.
  • Team coordination and management:
    • Direct and coordinate the work of individual contributors within the project team.
    • Assign tasks, monitor progress, and ensure efficient collaboration among team members.
  • Technical oversight:
    • Provide technical oversight and guidance on data science methodologies, tools, and techniques used in the project.
    • Ensure that the technical approach aligns with project objectives and TDSP best practices.
  • Stakeholder communication:
    • Serve as the primary point of contact for the project with stakeholders.
    • Communicate project status, progress, and any issues or changes to stakeholders regularly.
  • Problem-solving and decision-making:
    • Lead problem-solving efforts, addressing technical challenges and adjusting the project plan as needed.
    • Make key decisions affecting the project's direction and outcomes.
  • Quality assurance:
    • Ensure the quality and accuracy of project deliverables.
    • Implement quality control processes throughout the project lifecycle.
  • Risk management:
    • Identify potential risks to the project and develop strategies to mitigate them.
    • Manage and address issues as they arise, minimizing impact on the project.

Key tasks for the project lead

  • Schedule projects:
    • Create and maintain a detailed schedule of project activities and deadlines.
  • Allocate resources:
    • Allocate resources (human, technical, data) effectively to meet project needs.
  • Perform technical reviews and provide guidance:
    • Conduct technical reviews and provide guidance to team members on data processing, analysis, and modeling.
  • Monitor and Report:
    • Monitor project progress against goals and objectives.
    • Regularly report on project status to the team, stakeholders, and management.
  • Create documentation:
    • Ensure comprehensive documentation of methodologies, analyses, and results.
  • Facilitate meetings:
    • Organize and lead project meetings, reviews, and brainstorming sessions.
  • Train and support:
    • Provide training and support to team members as needed.
  • Comply with ethical standards:
    • Ensure adherence to ethical standards, data privacy regulations, and organizational policies.

Use language models and copilots

In the TDSP, the project lead is pivotal in driving individual data science projects toward their goals. Language models and copilots can significantly contribute to the project's success by enhancing decision-making, efficiency, and overall project execution. The project lead can integrate these tools to align with the TDSP framework in the following areas:

  • Detailed project management:

    • Project planning and scheduling: Use language models to help create detailed project plans, timelines, and scheduling, considering various project phases and milestones.
    • Task delegation and monitoring: Employ copilots to assign tasks to team members and monitor progress, ensuring adherence to the project timeline.

  • Technical oversight and decision making:

    • Technical research and validation: Use language models for researching and validating technical approaches, algorithms, and methodologies suitable for the project.
    • Decision support: Use language models to analyze various technical options and provide data-driven recommendations for critical project decisions.

  • Team coordination and support:

    • Team communication: Use language models for drafting clear and concise communication to keep the team aligned and informed about project objectives and updates.
    • Resource management: Employ copilots to track and manage the allocation and use of resources effectively within the project.

  • Quality control and assurance:

    • Code and model review: Use language models for automated code and model reviews, ensuring adherence to best practices and identifying potential issues or improvements.
    • Documentation review and enhancement: Use language models to help review and refine project documentation, including technical reports and user guides.

  • Stakeholder communication and reporting:

    • Progress reporting: Use language models to generate comprehensive progress reports and presentations for stakeholders, clearly communicating the status, challenges, and achievements of the project.
    • Stakeholder meeting preparation: Employ copilots to prepare for stakeholder meetings, including agenda setting, creating presentations, and summarizing key discussion points.

  • Risk management and issue resolution:

    • Risk analysis: Use language models to identify potential risks and develop mitigation strategies, ensuring the smooth progress of the project.
    • Problem-solving assistance: Use copilots and language models for brainstorming and developing solutions to address project challenges or bottlenecks.

  • Continuous improvement and learning:

    • Feedback analysis: Use language models to analyze feedback from team members and stakeholders, identifying areas for improvement in the project.
    • Process optimization: Employ copilots to refine project workflows, enhance efficiency, and implement best practices.

Summary

In the TDSP, the project lead is responsible for the detailed planning, execution, and management of data science projects. They play a key role in coordinating team efforts, providing technical guidance, managing stakeholder communication, and ensuring the quality and success of project outcomes.

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal author:

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These resources describe other roles and tasks in the TDSP: