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Data Science Manager vs Data Science Expert

Which profile does your company need? A comparison between the two roles: skills, responsibilities, and their role in the team.

16 Jul 2022 by Angelica Lo Duca

Photo by krakenimages on Unsplash

There’s a lot of debate in the tech world about whether data science managers or data science experts are more valuable to organizations. Some believe that managers are better able to develop and implement strategy, while others argue that experts are more skilled at working with data.

So, which is it? In this article, we’ll explore the pros and cons of each option to help you make a decision for your organization.

The article is organized as follows:

  • The roles of manager and expert

  • Do you need a manager or an expert?

  • Skills

  • Responsibilities

  • Working in a team

  • Do you need a manager or an expert?

The roles of manager and expert

The expert role is different from the manager’s because experts need a very deep understanding of algorithms and technologies from the area in which they have many years of experience, while managers only have a general knowledge of the topic. Very often, an expert doesn’t only have a good knowledge of technology, but also domain knowledge.

Data science managers are responsible for a team of data scientists and ensuring that projects are completed on time and within budget. They also work with clients to understand their needs and ensure that the team is meeting their expectations.

Data science experts are responsible for providing insights and recommendations based on data analysis. They also develop and test models to improve decision-making and help businesses solve complex problems.

Skills

There are a number of requirements that data science managers need to be aware of, both technical and non-technical. On the technical side, they need to have a good understanding of the various data science tools and techniques available, as well as how to use them effectively. They also need to be able to understand and interpret the results of data science experiments and provide feedback to the team.

Non-technical requirements for data science managers include good communication and interpersonal skills, as well as project management experience. They need to be able to work with different teams within an organization and be able to manage multiple projects simultaneously.

As a data science manager, it is important to also have technical skills. It is important to understand how it works but on a high level. This allows you to better understand the work your team is doing and to provide more effective guidance. Additionally, technical skills can help you build credibility with your team and improve your ability to troubleshoot issues.

Data science experts are often highly technical individuals with deep knowledge in a particular area. While this expertise can be invaluable, it is not always necessary for data science managers. In many cases, general management skills and an understanding of data science principles are sufficient.

Responsibilities

The responsibilities of a data science manager involve:

  • leading and coordinating a team of data scientists

  • developing data science strategies

  • overseeing projects from start to finish

  • directing the personal development of each team member.

Managers have a holistic approach to data science projects. They don’t only know and use technologies and models, but they also communicate with stakeholders and business users. They understand all the requirements of the machine learning models very well and how they will be used by business users. With this holistic approach, they should be able to identify new opportunities to leverage data science in various business areas.

As a data science manager, you will be responsible for developing and managing a team of data scientists. In addition to overseeing the work of your team, you will also be involved in business development and sales. This means that you will need to have strong communication and presentation skills. You will also need to be able to negotiate deals with clients and partners.

Data science experts, on the other hand, are responsible for executing data science projects. They possess in-depth knowledge of algorithms and statistical techniques and are able to wrangle large data sets. They also have strong communication skills, which are essential for collaborating with other members of the data science team.

As a data science expert, you will be responsible for conducting research and developing new techniques and methods for data analysis. You will also be involved in the implementation of these techniques into projects. In addition to your technical expertise, you will also need to have strong communication and presentation skills.

Working in a team

As a data science manager, one of your primary responsibilities is to develop and manage a team of data scientists. This includes hiring talented individuals, providing them with the resources they need to do their job effectively, and setting clear expectations. Additionally, you need to ensure that your team is working together harmoniously and effectively towards common goals.

As a data science manager, it’s important to be able to check your team’s work and make sure that they are on the right track. This means being able to understand the data and the analysis that they are doing. If you aren’t able to do this, then it will be difficult to manage the team and make sure that they are productive. A data science manager should also tell each member which direction he or she should take and tell them what the goals for the year or for the month are.

Data science experts are often able to provide insights and suggestions that can help improve the work of the data science team. In addition, data science experts are often able to build strong teams because of their deep understanding of the field. They know what skills and knowledge are necessary for success and can identify talented individuals. Data science managers, on the other hand, may not have the same level of expertise. While they may be able to identify potential team members, they may not have the ability to fully assess their skills and knowledge.

Do you need a manager or an expert?

The answer to this question may depend on the size and scope of your project.

If you have a large project with many moving parts, then you might need a data science manager to keep everything organized and on track. A manager can also be helpful in coordinating team members with different skill sets.

On the other hand, if your project is more focused and you have a clear idea of what you want to achieve, then you might be better off working with a data science expert. An expert can help you fine-tune your project and ensure success.

Ultimately, the decision of whether to work with a manager or an expert depends on your specific needs and goals. If you are unsure, it might be helpful to consult with both types of professionals to get a sense of what would work best for your project.

Anyway, you should keep in mind that if you hire first a data science manager, then the manager would basically build a data science team.

Summary

Congratulations! You have just learned the difference between data science manager and data science expert!

Data science managers are responsible for leading teams of data scientists and ensuring that projects are completed on time and within budget. They also need to have general technical skills and be able to effectively communicate results to non-technical stakeholders. Data science experts, on the other hand, are responsible for actually doing the work of data analysis and modeling. They need to have a deep understanding of statistics and machine learning and be able to effectively communicate their findings to both technical and non-technical audiences.

So, which role is right for you? If you feel like you have the leadership skills necessary to manage a team of data scientists, then a data science manager might be the right choice. If you’d rather focus on the actual work of data science, then a data science expert might be a better fit.

Ultimately, it’s up to you to decide which role best suits your job!

The content of this article has been inspired by the podcast episode Data Science Manager vs Data Science Expert with Barbara Sobkowiak at DataTalks.Club.

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