Cracking the Code of Data Science Team Structures
In today’s world, data is king, and having a Data Science team is like having a secret weapon for businesses and how you structure your Data Science team can significantly affect its effectiveness, efficiency, and ability to produce valuable insights.
That’s why it’s essential to have a well-structured team that can handle the complexity of the work and delivers high-quality results. In this article, we’ll explore some team structures for building a Data Science team that fits your needs.
A Data Science team is a group of smarty-pants who collect, analyze, and make sense of data to help companies make informed decisions. The team comprises different roles, such as data analysts, data engineers, machine learning engineers, and data scientists, each with unique skills.
For the sake of fun, let's represent these roles as:
I’ve been fortunate enough to work in a variety of data science setups over the years, from small startups to large enterprises, each with its unique approach to MLOps. Let's explore a few structures and discuss the ones I would suggest.
Structure 1 — Bottleneck
Imagine a Data Scientist who focuses on solving the mathematical aspects of a problem and produces a model as a solution. Once the model is complete, they hand it over to a team of Software Engineers. However, the Software Engineers don’t fully understand the model; their role is simply to deploy it, such as setting it up on an endpoint or as a scheduled job.
I guess you know why I am calling this a BottleNeck Structure 😉
Structure 2 — “MLOps will handle it”
Let’s say you have the luxury of having an MLOps team, which includes an ML Engineer or Software Engineer who understands what a model is and can create basic models), a Data Engineer who architects how data will reach the model and how it will leave it (i.e., training and inference), a DevOps person who ensures everything is stable and robust in production, a full-scale Data Scientist who understands the needs of the Data Science team and can address them, and a Project Manager for good measure.
In this scenario, the Data Science team handles the mathematical aspects of a project and hands over the completed package to the MLOps team, who understand models as well as software enough to deploy it as required.
The MLOps team is responsible for monitoring and maintaining the deployed model, and they can fix minor model issues. Major issues are reported back to the Data Science team for further analysis. Ideally, the MLOps team should provide input during the Data Science project cycle to ensure that the produced package is easily deployable and maintainable in production.
Structure 3 — The Wolf pack
In this team structure, trios consisting of one Software Engineer and two Data Scientists work together.
Each trio is responsible for producing and productionizing a model, with input and support from their peers in other trios. Depending on the complexity of the problem, multiple trios could work on smaller aspects of a larger Data Science problem.
Monitoring and maintenance of each component is the responsibility of the trio, with support from the DevOps team and existing DevOps tools in the organization w.r.t monitoring and alerting.
A few variations of this structure could include “Substituting an ML Engineer in place of a Data Scientist or the Software Engineer”
Structure 4 — Data Scientist on Steroids
Assuming that the Data Science team is mature enough to create models and can provide the packaged models to the DevOps team. The DevOps team treats this package as a container and deploys it with the required scale and robustness, based on the deployment specifications of the Data Science Team.
The DevOps team sets up the required monitoring and alerting, but both DevOps and Data Science teams act on these alerts. The alert specifications are laid down and acted upon by the Data Science team are model drifts and incorrect predictions whereas DevOps sets up and acts on scale-related alerts.
Having clear communication and collaboration between the DevOps and Data Science teams is crucial for the success of a project. If the Project Managers have a good understanding of technology, they can facilitate this communication and ensure that the expectations and goals of both teams are aligned. This can help to avoid misunderstandings and conflicts and can result in a more efficient and productive working relationship between the two teams.
Structure 5 — Babysit the Data Scientists
Alright, folks, we’ve covered a couple of team structures for Data Science so far, but hold on tight because this is the last one we’ll be discussing.
In this structure, the MLOps team develops an extension of the DevOps tools tailored to meet the specific needs of the Data Science team. The Data Scientist is responsible for producing the model, but they get a helping hand from the MLOps team to ensure everything runs smoothly during deployment.
The MLOps team is responsible for providing the Data Science team with the right tools and advice to empower them. The Data Science team lays down the deployment specifications with the help of the MLOps team, and they deploy the package with minimal effort.
The Data Science team takes care of monitoring and alerting, while the MLOps team only intervenes in scaling and robustness issues if needed (which, ideally, won’t be necessary).
So, here comes the big question
Which team structure should you choose for my data science team?
The answer is simple, yet complex — “it depends.”
But fear not, we’ve got you covered with some questions that will help you make a better decision:
- What are your business goals and how do they relate to your data science projects?
- What level of collaboration do you need between data scientists and software engineers?
- How important is it to have in-house expertise for the full range of data science activities, from data cleaning to model deployment?
- How much autonomy do you want your data science team to have in terms of project ownership and decision-making?
- How much technical debt are you willing to accept in your data science projects?
- How important is it for your data science team to be able to respond quickly to changing business needs?
- What level of support and resources do you have available for building and maintaining a data science infrastructure?
- How important is it for your data science team to be able to collaborate and share knowledge with other teams in your organization?
- What level of risk are you willing to accept in terms of model performance and potential errors?
- How important is it for your data science team to be able to explain and interpret their results to non-technical stakeholders?
Answering these questions will help you choose the best team structure that suits your organization’s needs. Keep in mind that there is no one-size-fits-all solution and that each structure has its advantages and disadvantages.
Ultimately, the key is to find the structure that allows your data science team to work efficiently and deliver the best results possible.
