How to Hire for AI, Data and Automation Roles in Australia

Australian businesses are adopting AI at pace. The Australian Bureau of Statistics reports that around 35 per cent of large businesses used AI in 2024-25, up from just 9 per cent in 2021-22, with adoption in financial and insurance services growing around 24-fold over the same period. As the technology spreads, so does demand for the people who build, deploy and maintain it. 

Here is the catch. Many of these roles stay open for months, and the cause is often the job advertisement rather than a lack of talent. Employers describe a single hire who can do the work of an entire team, then wonder why no one fits. Hiring well for AI, data and automation is less about chasing a rare candidate and more about designing a role a real person can fill. 

How do you hire for AI, data and automation roles? 

The most effective way to hire is to start with the problem you need solved, not a list of technologies. Once the problem is clear, the right skills, seniority and even the number of roles usually become obvious. Realistic roles attract more applicants, and better ones.  

A practical approach: 

• Start with the problem or outcome, not a list of technologies: the logistics business that needs dependable demand forecasting from its own data, not a research scientist. 

• Separate must-have skills from nice-to-have ones 

• Decide whether the work is really one role or several 

• Set a seniority level and salary that match the market 

• Describe the work itself rather than listing every possible tool 

The real reasons these roles go unfilled 

Two forces keep AI and data roles open. The first is real: demand is outstripping supply. The Future Skills Organisation projects a shortfall of around 130,000 digital experts and 242,000 digitally enabled workers across Australia. The second is self-inflicted: job descriptions that ask for too much. 

This second problem is often called the unicorn role, a single job that expects one person to research, build, analyse and run everything at an expert level. Very few people match that mix, so the role sits idle. In plainer terms, the advertisement describes someone who does not really exist. 

Common patterns include: 

• Asking for five years of experience in a tool that has existed for only three 

• Bundling a data engineer, a data scientist and an analyst into one hire 

• Requiring deep research credentials for work that is really about delivery 

• Setting the pay below the market for the skills being demanded 

The fix is not to lower your standards, but to be specific about what the role really needs. 

Getting the skills mix right 

For most businesses, the priority is people who can apply AI well, not invent it. The biggest gains come from using proven tools on real problems, so favour delivery over research credentials. Remember too that data, not modelling, is usually the bottleneck, so strong data skills often matter more than the latest algorithm. 

Knowing what the main roles do makes the right mix clearer, since it depends on your problem: 

• Data engineers build the pipelines that move and store data 

• Data scientists and machine learning engineers build and deploy models 

• Automation engineers connect tools and systems to remove manual work 

• Analysts turn data into decisions the business can act on 

When you prioritise, put the practical skills first: 

• Building, deploying and maintaining solutions in production 

• Solid data handling and engineering fundamentals 

• Clear communication and the ability to frame a business problem 

• Familiarity with the specific tools already in your stack 

Treat advanced research and niche specialisms as nice-to-have. For most hires, they are not what decides success. 

Should you hire one specialist or build a team? 

It depends on the maturity of your work. Early on, a versatile generalist who can build end to end often delivers more value than a narrow specialist. As the work grows, specialisation pays off, and you build a team across data engineering, modelling and deployment. Match the hire to the stage you are at now, not the stage you aspire to. 

Frequently asked questions 

What is a unicorn role in tech hiring?  

A unicorn role asks one person to hold every relevant skill at a high level, such as research, engineering, analysis and operations combined. These roles are hard to fill because few candidates match them, and they often sit open for months. 

What is the difference between a data analyst, data engineer and data scientist?  

A data analyst interprets data to inform decisions, a data engineer builds the systems that move and store data, and a data scientist builds models to predict or classify. Many stalled roles try to combine all three. 

Do you need a PhD to work in AI?  

No. Most applied AI and data roles value practical delivery over academic credentials. A PhD helps for research-focused positions, but for building and deploying solutions, hands-on experience usually matters more. 

Ready to hire for AI, data and automation? 

The employers who win this talent are not the ones with the longest requirements list. They are the ones who scope roles clearly and move quickly. At Fuse Recruitment, we help employers design realistic AI, data and automation roles and connect with people who can deliver them. If you are planning a technology hire, get in touch to shape a role that attracts the right candidates rather than deterring them.