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Building an AI team can be daunting, especially when you’re unsure about the right talent to hire first. In this article, we’ll first explore the roles of data analysts, data scientists, and data engineers, and discuss the pros and cons of hiring each profile. We’ll also delve into how these roles fit into your company culture and help you build a robust data team.

What Role Does Each Profile Play?

Let’s explore each data role, assuming they are playing a part in a startup team.

Data Analyst: The CFO of Your Data Team

Data analysts are like the CFOs of your data team. They understand your company’s inefficiencies, identify areas of excellence, and highlight underperforming sectors. They can draw potential scenarios and forecast outcomes, providing a self-assessment of your processes.

Hiring a data analyst first can foster a culture of introspection and retrospective analysis, helping your team trust and understand data. However, the main disadvantage is that improvements in profit will be incremental, not continuous.

Data Scientist: The CEO of Your Data Team

Data scientists are akin to the CEOs of your data team. They define the vision and aim for exponential growth or improvement. They work with machine learning, which tends to improve over time as more data is gathered.

Hiring a data scientist first can lead to a linear, if not exponential, increase in profit. However, without the support of other profiles, you may hit a plateau in improvement or even experience a drop due to accumulated technical debt.

Data Engineer: The COO of Your Data Team

Data engineers are like the COOs of your data team. They define the processes that lead to sustainable and scalable growth. They can prevent the plateau or drop in improvement that can occur when relying solely on a data scientist.

Hiring a data engineer first can provide the right infrastructure for your AI initiatives. However, this approach can be costly and may commit you to a path before you’ve validated whether AI is right for your company.

So, Who Should You Hire First?

While some argue that you should hire a data engineer first to build the right infrastructure, I believe starting with a data scientist or a data analyst is better. This approach allows you to build a data-driven culture, validate whether AI is right for your company, and identify the best business processes to tackle with AI.

Once you’ve validated that AI is right for you and identified the best use cases, you can then hire a data engineer to build the necessary infrastructure. This approach allows you to validate your AI initiatives, achieve short-term profit, and reinvest that profit in building the right infrastructure.

Conclusion

Building an AI team is a strategic process that depends on your company’s mindset and priorities. Whether you choose to hire a data analyst, data scientist, or data engineer first, the key is to validate your AI initiatives, build a data-driven culture, and invest in the right infrastructure.

Remember, there’s no one-size-fits-all approach. The right strategy for your company will depend on your specific needs and circumstances. If you need help figuring out the best approach for your company, don’t hesitate to reach out to us at NILG.AI. We’re here to help you navigate the complexities of building an AI team and implementing AI in your business.

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