Starting an AI-focused company is exciting yet complex, and building the right team is one of its biggest challenges. Unlike traditional tech startups that primarily need versatile developers (like front-end, back-end, and DevOps), AI startups demand specialised talent. Data and AI roles—such as data scientists, machine learning engineers, and data analysts—require deep technical and often academic expertise that goes beyond what general developers can typically provide.

While some roles overlap, others are unique to the AI landscape, and each plays a crucial part in moving the startup toward its goals. Balancing budget constraints with the need for specialised AI talent can be challenging, especially in the early stages. Let’s explore the essential data profiles in an AI startup, their contributions, and how to strategically build this team from the ground up.

Essential data profiles

Creating an effective AI team means understanding the different roles and how they contribute to your product. Here’s an overview of the key profiles needed in a data-driven company:

  • Data Scientist: Known as the go-to role for making sense of data, data scientists bring expertise in designing and testing models, especially in predictive analytics and machine learning. They uncover insights, validate hypotheses, and drive product direction through complex data analysis. In smaller teams, a data scientist may also take on data analyst tasks, balancing cost-effectiveness with advanced technical expertise.
  • Data Analyst: Data analysts focus on analysing and interpreting data to generate actionable insights. They work closely with the startup's business side, creating reports, visualisations, and recommendations that support decision-making. While this role may not be as technical as that of a data scientist or engineer, it provides essential support in turning data insights into business strategies.
  • Data Engineer / ML Ops Engineer: Data engineers are responsible for building and maintaining the infrastructure required for data collection, storage, and processing. They ensure data flows efficiently across the organisation, creating ETL pipelines that prepare data for further analysis or model training. In a startup setting, this role often overlaps with Machine Learning Ops engineering, where they also operationalise machine learning models, manage workflows, and ensure scalability.

Each role brings unique value to an AI startup. However, knowing which to prioritise can be crucial, especially when resources are limited.

Which profile should you hire first?

The order in which you hire for these roles will depend on your product's nature and the company’s immediate goals. Here’s my recommended approach:

Start with a Data Engineer: Your first hire should focus on establishing the necessary infrastructure for running your AI applications. In the early stages, a skilled back-end developer can create initial data pipelines, allowing you to collect, clean, and store data for future AI applications. This person can also implement integrations with off-the-shelf AI tools (such as OpenAI) to quickly bootstrap your product for early production use.

Increase quality with a Data Scientist: Hiring a data scientist elevates your product quality. They’ll be responsible for data cleaning, model building, and refining these processes. Additionally, data scientists often take on data analyst duties in startups, covering everything from generating insights to building models. This dual role is cost-effective and ensures that data needs are met from both strategic and analytical perspectives until you’re ready to hire a dedicated data analyst.

Overview of the dream team

For an AI startup, a lean and versatile team can be highly effective. Here’s a suggested team structure:

  • Product manager: Translates business needs into technical requirements for the team.
  • Back-end developer: Designs and implements APIs, manages server-side logic, and ensures seamless integration with front-end applications.
  • Data engineer: Builds and maintains pipelines for data ingestion, transformation, and storage to support analytics and AI workflows. Deploys machine learning models at scale.
  • Front-end developer: Creates the user interface and visualises data and AI outputs.
  • Data scientist/data analyst: Develops and refines AI models, ensuring both model quality and relevance.

In an early-stage startup, these roles are often combined, with team members taking on multiple responsibilities. For instance, a back-end developer might also handle data engineering tasks. As the startup grows, this structure can evolve, introducing dedicated roles to ensure seamless product delivery and scalability.

Conclusion

Building the ideal AI team requires flexibility and strategic prioritisation. Start with roles that establish a strong foundation, then hire specialised talent aligned with your AI ambitions. Initially, data engineers or back-end developers are often the best choice, followed by data scientists who bring analytical depth and help establish a foundation for machine learning.

With a clear hiring roadmap and a well-structured team, you can effectively navigate the talent landscape, expand your AI capabilities, and drive your startup toward success.