Project management for data science

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Are you wondering what project management skills are important for data scientists and developers to learn? Well then you are in the right place! In this article, we discuss what project management skills data scientists must learn and why they must learn them.

We start out by discussing why project management skills are important for data scientists and engineers to develop. After that, we lay out the most important project management skills that data scientists and engineers must master. Finally, we go over a few important caveats that make project management particularly difficult for data science projects.

Why are project management skills important for data scientists?

Before we discuss what project management skills are most important for data scientists, we will first discuss why project management skills are important for data scientists.

  • Align on project vision. The first reason that project management skills are important for data scientists is that they help data scientists ensure that everyone who is involved in a project is aligned on the project vision. If the entire project team is aligned on the project vision, this ensures that everyone is pushing in the same direction to achieve the same result. If alignment is not achieved, some parts of the project may need to be reworked multiple times before they can be put into use. Data science projects are not useful unless they are put into use by partners across the business. It is crucial that data scientists align with these partners before they complete a project so they can be sure that they are delivering something useful in the correct format. This ensures that no one spends their time on unnecessary work that will need to be redone later.
  • Deliver results on time. Project management skills are also important because they help data scientists map out timelines for their projects, and more importantly, ensure that these timelines are achievable. Delivering projects on time helps to build trust with stakeholders and helps the team develop a reputation for being dependable. This reputation will ensure that the team has more opportunities to work on high impact projects.
  • Focus on the most important projects. Another reason project management skills are important for data scientists is that they help data scientists ensure that they are focusing on the most impactful and important work they can be doing. This helps to ensure that the team is delivering value to the company, which provides managers with more ammunition when it comes time to advocate for team growth and compensation raises.
  • Get recognition. Just focusing on the most important project is not enough if no one else knows about the value the projects are delivering value to the company. Project management skills are also important because they can help to ensure that data scientists get proper recognition for the work they deliver. This helps to ensure that the team gets rewarded for their hard work.

What project management skills are important for data science?

Now that we have discussed why project management skills are important for data scientists, we will discuss which project management skills are most important for data scientists to master. Here are the most important project management skills for data scientists and developers.

  • Prioritization. The first project management skill that is important for data scientists to master is prioritization. The need for prioritization can show up at many different levels. On a day-to-day basis, data scientists who are balancing multiple differ projects or requests from multiple different stakeholders need to prioritize which tasks they will work on that day. When planning team roadmaps, data scientists may also need to prioritize different project ideas to determine what they will work on first. Data scientists must learn to prioritize their work so that they can ensure they are working on the most impactful projects.
  • Estimating project timelines. The next project management skill that is important for data scientists is estimating timelines. Timeline estimation is particularly important for projects that also require contributions from other teams. If the timeline for the data contributions is well scoped out, this can help to inform external teams of when they will need to make their contributions. Timelines can also be an important factor that plays into how projects get prioritized. Given two projects with similar levels of impact, data scientists should generally prioritize the project with the shorter timeline so that they can deliver more value in less time.
  • Breaking down large problems. Another important project management skill that data scientists need to master is breaking down large problems into smaller chunks. Data projects are often best received when their deliverables can be broken down into a few distinct milestones that can be delivered incrementally. This makes it easier for data scientists to demonstrate the progress that they are making on the project and often allows for some value to be delivered early on. Breaking down large problems into smaller chunks can also be an important exercise for scientists who are attempting to estimate project timelines.
  • Technical documentation. Writing good technical documentation is also an important skill that is crucial for data scientists. Producing good technical documentation is often the fasted way to ensure that a project team is aligned on the project vision and ensure that everyone is working on the correct thing. Good technical documentation also provides an avenue for other data scientists and collaborators to provide feedback on the project plan.
  • Communication & negotiation. Communication and negotiation are also project management skills that are critical for data scientists. These skills help data scientists to ensure that the right resources are available and that everyone is working on the right problem. They also help data scientists build trust with non-technical stakeholders who are skeptical of data science methods and models. The better data scientists can communicate the importance and results of their projects with non-technical stakeholders, the more likely the data science is to be approved to work on high impact projects.
  • Risk identification & management. A final project management skill that is important for data scientists is risk identification & management. Data scientists must learn to identify roadblocks that are coming ahead of time so they can make a plan to deal with them. If the roadblocks are not so easy to foresee, data scientists must be able to adapt and develop backup plans that they can execute on. This helps to ensure that data scientists can deliver projects on time.

What makes data science project management different?

Now we will talk about a few caveats that make project management for data science projects different from project management for other disciplines. Here are a few reasons that data science projects are particularly difficult to manage.

  • Balance exploration with delivery. One of the factors that makes project management particularly difficult for data science projects is that there is a lot of exploration that needs to be done during data science projects. This makes it particularly difficult to estimate project timelines because data scientists do not know whether the first method they try will work or whether they will have to try many different methods before they settle on one that works for their use case. Data scientists should make sure to plan their timelines accordingly and leave sufficient room for the exploration phase when creating project roadmaps.
  • More uncertainty around project feasibility. Along the same lines, there is more uncertainty around data science projects because there is no guarantee that the problem they are working on will be solvable. It is always possible that a data scientist will find that the data they are working with is all noise and no signal. This makes it more difficult to deal with stakeholders in the early stages or projects because on one hand, data scientists want to get stakeholders excited about the projects they are working on, but on the other hand, data scientists do not want to get stakeholders too excited about a solution they might not be able to deliver. It is important to set expectations with stakeholders appropriately to maintain a positive reputation for the data science team.
  • Less trust among non-technical stakeholders. A final caveat that makes project management difficult for data science projects is that non-technical stakeholders are often skeptical of complex machine learning models that they do not understand. This means that data scientists have to spend a lot of time and effort building trust with stakeholders.


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