{"id":194173,"date":"2025-07-11T15:14:22","date_gmt":"2025-07-11T15:14:22","guid":{"rendered":"https:\/\/www.harnham.com\/?p=194173"},"modified":"2025-07-24T10:33:32","modified_gmt":"2025-07-24T10:33:32","slug":"15000-placements-global-data-ai-insights","status":"publish","type":"post","link":"https:\/\/www.harnham.com\/15000-placements-global-data-ai-insights\/","title":{"rendered":"15,000 Placements Later: What We\u2019ve Learned About the Global Data & AI Talent Market"},"content":{"rendered":"

\"\"\u00a0 By David Farmer,<\/a> CEO, Harnham Global<\/em><\/p>\n

We haven\u2019t just hit 15,000 placements, we\u2019ve launched 15,000 careers, changed 15,000 lives and it\u2019s a marker. A marker of how far the Data & AI industry has come and what it\u2019s taken to get here. From our very first Infrastructure Analyst in 2006 to placing CDOs, Directors of Data, AI Directors and Heads of AI, we\u2019ve been on this journey with the industry since the beginning.<\/span><\/p>\n

We\u2019ve seen it all: the early days of SAS and SQL, the shift to Python and the cloud, the growth of ethics and governance roles, and the ongoing challenge of finding people who cannot just crunch numbers but tell stories with data. And recently, we have seen another shift – the move from the generic \u2018Data person\u2019 role, required to wear multiple hats (analysts, developers, engineers), to more specialised roles with defined purpose such as \u2018Data Engineers\u2019 \u2018BI Analysts\u2019 and \u2018AI engineers\u2019.\u00a0<\/span><\/p>\n

Now, we\u2019re sharing intel about what we\u2019ve learned and where we think it\u2019s all going next.<\/span><\/p>\n

What\u2019s Changed?<\/b><\/h3>\n

Of course, the obvious is the size and scale of the industry. According to ChatGPT, the number of roles in data has grown from 1-2 million in 2006 to somewhere between 15 million and 25 million today. That change has had a huge impact on the roles within the industry, moving the data science role from predominantly academia or statistics-based work to a much broader role within a huge spectrum of industries. The Data scientist title became synonymous with one of the zeitgeist moments of the data boom, with the \u201cneed\u201d for a data scientist or the desire to be one coming in advance of a true definition of the role. This was borne out in the data analysis for our salary guides through that period, with candidates with the title Data scientist performing roles which would traditionally have been called Marketing Analyst, CRM Analyst or even Data Governance manager. <\/span>
\n<\/span>
\n<\/span>The shift in Governance is another ongoing shift. Historically viewed as a compliance \/ audit function, with the growth of Data and more recently a boom in AI deployment, this role has shifted in scope and placement, moving from an audit position to a centralised role of data stewardship, MDM and AI ethics. The latter will continue to grow, with Cambridge University\u2019s MPhil in Ethics of AI, Data and Algorithms showing what we believe to be a necessary direction of travel.\u00a0<\/span><\/p>\n

Day to day, Harnham started out by placing analysts who could build dashboards and run reports. Now, we\u2019re placing AI Engineers, MLOps specialists, and those designing LLMs. The pace has accelerated. The roles are deeper. The stakes are higher. The tools have changed, too. From SAS, Excel and VBA to Docker and Kafka. From structured datasets to messy, real-time, unstructured data. There\u2019s a shift from siloed analytics to fully embedded teams, and from back-office reporting to real-time, business-critical decisions.<\/span><\/p>\n

In further articles we will focus in on individual roles, responsibilities and titles to highlight the shift seen within each part of the industry. \u00a0<\/span><\/p>\n

\"\"<\/p>\n

The graph shows the percentage increase or decrease in the number of roles Harnham has filled with ‘AI’ in the job title over the years.<\/em><\/p>\n

What Hasn\u2019t Changed?<\/b><\/h3>\n

One thing that\u2019s remained consistent: the ability to take what the data is saying and tell someone else about it in a way that matters. That\u2019s always been the difference-maker. It\u2019s never just been about the code or the model – it\u2019s about translating complex insights into business action. Finding people who can bridge that gap, who can make sense of numbers and influence decision-making, remains one of the toughest challenges, just like it was when we started.\u00a0<\/span><\/p>\n

We will be covering this in a whitepaper, which will be available soon.<\/span><\/i><\/p>\n

\"\"<\/p>\n

Yes, there\u2019s growing demand for Chief AI Officers and Directors of AI. But our 15,000th placement? A Rockborne-trained Business Data Analyst. Proof that the next generation coming through still underpins it all. Looking at that role and candidate in more detail tells us a lot about how the industry has moved. <\/span>
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\n<\/span>Meeting Abby, candidate 15,000, we learned that no one day is the same – some days the focus is all about engaging and presenting to stakeholders, other days focus on managing projects, diving into code to help teams out, building power BI dashboards for clients, and other days the focus is to study data lineage and architecture diagrams.<\/span>
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\n<\/span>That breadth of skill is something companies seek at the junior talent levels, so the breadth of training Abby went through prior to her starting in role has enabled her to be more successful.\u00a0<\/span><\/p>\n

Her ability to communicate complex outcomes in a clear business language and easy to understand way highlights the importance of the story telling point from earlier.\u00a0<\/span><\/p>\n

Overall though, these 15,000 candidates don\u2019t just highlight a tech journey. They show a story of capability. A move from reporting to real-time insight. From projects to products. From centralised teams to embedded ones. Some things change. Some things stay the same.<\/span><\/p>\n