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
- Skills comparison
- Responsibilities
- Working in a team
- When to hire 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
Data Science Manager Skills
Data science managers need a blend of technical and non-technical skills:
Technical Skills:
- High-level understanding of data science tools and techniques
- Ability to interpret experiment results and provide feedback
- Sufficient technical depth to build credibility with the team
Non-Technical Skills:
- Strong communication and interpersonal abilities
- Project management experience
- Ability to work with different teams and manage multiple projects
- Strategic thinking and planning
While managers don’t need the same depth as experts, technical understanding helps them guide their teams effectively and troubleshoot issues.
Data Science Expert Skills
Data science experts are highly technical individuals with deep knowledge in specific areas:
- Deep expertise in algorithms and statistical methods
- Advanced machine learning and modeling techniques
- Programming proficiency (Python, R, SQL)
- Research and experimentation capabilities
- Domain-specific knowledge
This specialized expertise is invaluable for solving complex problems, though general management skills and understanding of data science principles are sufficient for managerial roles.
Responsibilities
Data Science Manager 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 communicate with stakeholders and business users, understand machine learning model requirements, and identify new opportunities to leverage data science across business areas. In addition to team management, they’re often involved in business development and sales, requiring strong communication, presentation, and negotiation skills.
Data Science Expert Responsibilities
Data science experts are responsible for executing data science projects. They possess in-depth knowledge of:
- Algorithms and statistical techniques
- Large-scale data wrangling and processing
- Research and development of new analytical methods
- Implementation of advanced techniques into production systems
While their focus is technical depth, experts also need strong communication skills for collaborating with team members and explaining complex findings.
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 review your team’s work and ensure they’re on the right track. This requires understanding the data and analysis they’re performing. Without this capability, it becomes difficult to effectively manage the team’s productivity. Managers should provide clear direction to each team member and communicate goals for the year or month.
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.
You should keep in mind that if you hire a data science manager first, they can then build and scale the data science team according to your organization’s needs.
Summary
Congratulations! You have just learned the difference between data science manager and data science expert!
Data Science Managers:
- Lead teams and ensure projects are completed on time and within budget
- Need general technical skills and strong communication abilities
- Focus on strategy, team development, and stakeholder management
Data Science Experts:
- Do the actual work of data analysis and modeling
- Need deep expertise in statistics, machine learning, and algorithms
- Focus on building and implementing advanced analytical solutions
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 needs! The decision depends on your organization’s current stage, project complexity, and whether you need strategic leadership or specialized technical execution.
This article is based on the podcast episode Data Science Manager vs Data Science Expert with Barbara Sobkowiak at DataTalks.Club.