We need to talk about the job postings flooding LinkedIn right now. 

Companies want someone who can do AI strategy, data architecture, machine learning engineering, GenAI implementation, data visualization, and cloud infrastructure. Bonus points if you know Microsoft Azure, AWS, GCP, Databricks, Snowflake, Power BI, Tableau, ChatGPT, Claude, and about fifteen other platforms. 

Here’s the problem: that person doesn’t exist. 

The data proves it. 57% of data scientist job postings ask for versatile professionals who can handle far more than core data science abilities. We’re not just looking for experts anymore. We’re hunting unicorns. 

So what actually happens when you post that impossible job description? 

You Get a Generalist Who Over-Promises 

They interview well. They’ve touched enough technologies to sound credible. They get hired. 

Then they get stuck fast. 

Maybe they quit when they realize the role is impossible. Maybe they get demotivated watching projects stall. Or worse, they build something that isn’t secure or compliant because they simply don’t have the depth of expertise required. 

And here’s the terrifying part: they’re making critical decisions by Googling answers. 

No one’s around to ask for help except ChatGPT. So they make it up as they go. Getting it wrong isn’t just possible. It’s inevitable. 

The security risks are real. Research shows 55% of generative AI inputs contain sensitive personal information, and studies found that 40% of AI-generated code suggestions lead to security vulnerabilities. When your solo hire doesn’t know what they don’t know, your organization pays the price. 

The Math Doesn’t Work Either 

Let’s talk about what that full-time hire actually costs. 

Salary, pension, benefits, sick leave, recruitment costs, management time. For a senior data scientist, you’re looking at £100k per employee per year or more. That’s before you factor in the 142-day average hiring timeline. 

And you’re paying that whether you need them every day or not. 

Most data and AI work isn’t needed full-time. You need expertise to set things up, troubleshoot when things break, or implement new capabilities. The rest of the time? That expensive hire is answering emails and sitting in meetings. 

There’s a Better Model 

Instead of one generalist who knows a little about everything, you can access a team of experts who each know one area deeply. 

These specialists spend all their time in their domain. They know the common pitfalls. They know best practice. They get it right the first time, backed up by a team that quality-controls their work. 

The learning curve concern is overblown. Most businesses are remarkably similar when it comes to technology. You have customers who buy things. You want data to be secure. The technology patterns repeat across sectors, even if the business models differ. 

You pay for expertise one day at a time, only when you need it. No sickness coverage. No resignation risks. No management overhead. Just specialized knowledge exactly when required. 

The Simple Rule 

Recruit full-time for roles you need every single day. 

Everything else? Access it on demand. 

Stop chasing unicorns. Start building a network of specialists who actually exist.