Resolving conflicts between different teams is critical to project success and should be ... Implement a unified data and AI platform: Centralize resources to avoid duplication of effort and enable both teams to collaborate with the same data, tools and deliverables in real time. Establish clear project roles: Define the responsibilities of each team within the project to minimize overlap and increase focus. This ensures that engineers manage pipelines and infrastructure while data scientists focus on model development. Prioritize aligned goals: Encourage shared project goals to foster collaboration and make business impact and stakeholder value the key metrics for success.

To resolve resource conflicts between data scientists and engineers, establish clear project priorities and roles. Facilitate open communication to align on shared goals, and implement a collaborative workflow, such as Agile or Scrum, to enhance flexibility. Use project management tools to track resource allocation and progress transparently. Consider designating a project manager to balance competing demands and mediate conflicts. Regularly review resource needs and adjust based on feedback, ensuring both teams have what they need to contribute effectively to project success.

You will always have this problem with one team serving multiple projects. Distribute the work according to the experience. Motivate those to take additional work who are smart and can finish faster, but incentivise them. Negotiate with business or stakeholders on timelines upfront for projects with less impact, explain the risks and challenges so you can get some breathing time for critical projects

To ensure project success amid resource conflicts between data scientists and engineers, I would focus on clear communication, prioritization, and collaboration. First, establish shared goals and align priorities through regular cross-functional meetings. Assign roles and responsibilities to minimize overlap, ensuring each team understands its scope. Implement a project management system to track tasks and deadlines transparently, making resource bottlenecks visible early. Foster collaboration by encouraging joint problem-solving sessions and providing tools for seamless communication. If necessary, negotiate additional resources or adjust timelines with stakeholders to balance workload effectively.

Unified Data and AI Platform: Centralize resources to eliminate duplication of effort, allowing both teams to work with the same data, tools, and deliverables in real-time. Clear Project Roles: Clearly define each team’s responsibilities within the project to minimize overlap and maintain focus. This ensures that engineers handle pipelines and infrastructure, while data scientists concentrate on model development. Aligned Goals: Foster collaboration by setting shared project objectives, ensuring that the focus is on achieving business impact and delivering value to stakeholders as the primary metrics for success.