Analytics Leadership in PE Portfolios: Timing the Right Hire

A Head of Analytics can play an important role in translating data capability into measurable value creation inside a portfolio company. For operating partners, the question is not simply when to hire, but how analytics leadership fits within broader portfolio design and value creation priorities.

Across private equity portfolios, firms are investing in data cube architecture, KPI standardisation, and portfolio-wide reporting infrastructure. Even where central infrastructure exists, execution inside each company still depends on leadership capability at the operating level.

Introduced at the right stage, analytics leadership can improve decision quality and strengthen operating discipline. Introduced without clarity on mandate or readiness, it can add structure without measurable impact.

On this page

• Why analytics leadership matters in portfolio design
• When to hire a Head of Analytics
• What operating partners should look for in analytics leadership
• Permanent vs interim leadership across the hold period
• How to align analytics leadership to portfolio infrastructure
• What this means for operating partners

Why analytics leadership matters in portfolio design

Many PE firms are building portfolio-level reporting environments, data cubes, and standardised KPI frameworks. These initiatives improve visibility across assets and support fund-level performance management.

However, portfolio infrastructure does not replace operating leadership inside each company.

At company level, analytics leadership is responsible for:

• Translating portfolio KPIs into operating priorities
• Aligning analytics work to commercial and operational decisions
• Ensuring local execution reflects the investment thesis

Without this execution layer, reporting can improve while operating decisions remain unchanged.

For operating partners, the question becomes: where does leadership accountability sit between portfolio-level data architecture and company-level execution?

When to hire a Head of Analytics

Private equity ownership alone does not automatically require a Head of Analytics. The role becomes relevant when growth stage and operational complexity create coordination challenges.

When analytics leadership becomes necessary

Hiring before these signals emerge can create unclear mandates. Waiting too long can leave analytics reactive rather than integrated into operating cadence.

What operating partners should look for in analytics leadership

In PE-backed environments, analytics leadership must operate at the intersection of execution and value creation.

Key attributes typically include:

Commercial orientation

Leaders must understand how analytics connects to revenue quality, margin expansion, cash flow, and operating efficiency.

Stakeholder credibility

The role requires influence across CFOs, COOs, commercial leads, and board-level stakeholders.

Scale-stage awareness

Experience operating in growth environments is important. The balance between structure and speed shifts over the hold period.

Across Harnham’s work with PE-backed companies, effective analytics leaders are rarely defined by technical depth alone. Impact depends on their ability to prioritise use cases tied directly to value creation.

Permanent vs interim leadership across the hold period

Not every business requires a full-time hire immediately.

Market data suggests that permanent hiring remains the dominant model, but operating partners often use interim leadership early in the hold period to clarify scope before scaling.

How to align analytics leadership to portfolio infrastructure

As firms invest in portfolio-wide data cubes and reporting layers, clarity of responsibility becomes critical.

Operating partners should distinguish between:

• Portfolio infrastructure – centralised reporting, KPI standardisation, fund-level analytics
• Company execution – embedding analytics into pricing, operations, forecasting, and daily decision-making

A Head of Analytics operates primarily in the second category. Even in well-instrumented portfolios, execution risk remains unless leadership translates reporting insight into operating change.

What this means for operating partners

Analytics leadership should not be mandated uniformly across all portfolio companies. Instead, decisions should be guided by stage, complexity, and value creation priorities.

Operating partners may consider:

• Should analytics leadership be introduced in every asset, or only those with complexity thresholds?
• Is interim leadership appropriate early in the hold period while infrastructure stabilises?
• How do we avoid over-hiring before data foundations are mature?
• How will leadership impact be measured against the value creation plan?

Success is rarely defined by dashboard delivery. It is measured by improvements in decision quality, operating consistency, and alignment to the investment thesis.

Many firms use market data, such as Harnham’s Data & AI Hiring Guide, to sense-check expectations and retention risk across the hold period.

How Harnham supports analytics leadership across PE portfolios

Across private equity portfolios, the challenge is less about recognising the importance of analytics and more about structuring leadership appropriately across assets.

Harnham works with operating partners and portfolio leadership teams to:

• Define analytics leadership mandates aligned to value creation plans
• Benchmark role scope and seniority at different growth stages
• Introduce interim or permanent leadership in line with hold-period priorities

Our focus is on ensuring analytics leadership supports portfolio design rather than adding complexity without clear accountability.

For firms reviewing analytics leadership needs, you can explore Harnham’s analytics hiring capabilities or speak with our team for a market-led discussion.

Last updated: 2026

Written by Harnham’s Analytics & AI leadership team.

The post Analytics in Private Equity: Driving Portfolio Value appeared first on Harnham.

A major privately owned media organisation in Europe, employing more than 12,000 people and operating hundreds of print and digital brands, had reached a critical point in its data journey. As its digital footprint expanded, so did its data capability, but not in a way that created cohesion. 

Data teams across multiple countries and functions were working hard, but independently. Analytics, planning, digital, infrastructure and legal each had their own tools, processes, and priorities. The organisation needed one view of its audiences and a consistent, streamlined approach to how data informed decisions across the group.

The Challenge

Fragmented data functions made it difficult to share insight, reduce duplication, or measure impact at group level.

The client wanted to unify its data teams under one strategic direction enabling the UK data function to act as an internal analytics consultancy to the wider business.

To do that, they needed a senior leader who could connect the dots: someone able to define best practice, align teams across markets, and embed data into everyday commercial decisions.

The Partnership

Harnham was engaged to identify a Head of Data Science & Analytics who could combine strategic thinking with hands-on delivery.

The search was entirely headhunt-led. From taking the brief to verbal acceptance, the full process took just 30 days. During this time, we:

• Identified 12 targeted profiles

• Secured 8 first-stage interviews

• Progressed 6 second-stage interviews

• Delivered 3 final-stage interviews

Candidates were evaluated for their technical depth, leadership across multidisciplinary teams, and experience in fast-moving, consumer-facing environments.

The Delivery

Once the Head of Data Science & Analytics was in role, the next step was to build the structure needed to deliver their roadmap.

Harnham supported the wider transformation by placing 17 additional hires. A mix of data engineers, analysts, and data scientists to strengthen capability across the group.

This combination of executive search and delivery support meant the new leader could focus on execution from day one, not recruitment.

The Impact

Since the appointment, the organisation has:

• Unified data teams across the UK and Europe under a single operating model.

• Delivered a clear data strategy and roadmap for audience analytics and personalisation.

• Reduced reliance on external agencies, building in-house capability and improving cost efficiency.

• Accelerated reporting and insight cycles, helping business units act on data faster.

• Improved segmentation and targeting, directly supporting audience engagement and monetisation.

The Bigger Picture

The client now operates with a more connected, consistent approach to data. The UK team acts as a central hub for analytics across Europe driving consistency, improving visibility, and supporting better commercial decisions across the business.

For Harnham, this project demonstrates what we do best: bringing together executive search precision and delivery capability to help clients build lasting data teams and leadership at scale.

Hiring across Data Teams? Get Practical Support

Book a consultation with a hiring specialist who understands the market.

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By Guillian Eller, Manager AI/ML – Harnham

You’re growing fast. You needed data talent yesterday. 

The reality in today’s competitive startup job search is that a highly specific role can get filled in 15 days. But without a plan for developing your specialist teams, the pattern of poor performance, constant crises, and increasing employee turnover will keep repeating itself.

If these sound familiar, you may be in need of some help with hiring for startups.

With a tight and proactive process (and the right partner), you can move from scoping to a signed offer in two weeks or less, without lowering the bar. 

The Common Challenges in Fast Data Hiring

Hiring data talent feels like a race against time. You need people who can ramp up fast and make an impact, but speed brings risk.

Many founders tend to focus on growth in their new markets and products, and inadvertently overlook the critical need for strategic hiring.

Here’s what often goes wrong:

• Unclear scope: roles are defined too broadly or too late, so the search starts without alignment.

• Limited bandwidth: hiring sits alongside fundraising, client delivery, or product deadlines.

• Fierce competition: the best candidates are fielding multiple offers and vanish fast.

• Cultural mismatch: someone might be technically brilliant but struggle in a lean, fast-changing environment.

Hear from Kiran Ramasamy on the confusion around AI Hiring:

Start with Strategy: Assess Your Current Team

Before adding new hires, take stock of the team you already have.

Ask yourself:

• Which people are doing two jobs well, and which are stretched too thin?

• Who has the potential to grow into a bigger role with support?

• Where are the gaps that will widen as the business scales?

Understanding where they can evolve, and where you’ll need new capability, is the first step in building a scalable data function.

After all, not every skills gap requires an external hire. Sometimes, the faster route is to upskill or reskill the team you already have. Data professionals today are investing heavily in learning and supporting that growth internally can be a powerful retention and performance strategy.

Once you’ve identified which skills can be developed internally, you can focus your hiring on the gaps that truly need external expertise — often saving time, cost, and onboarding friction.

We explored this in our recent article: Reskilling and Upskilling: What US Data Professionals Are Doing

Best Practices for Fast, High-Quality Hiring

Here’s what we’ve seen consistently drive results for scaling data teams:

1. Define the problem the role will solve. Clarity at the start shortens every step after it.
2. Keep assessments practical. Use short, business-relevant exercises that test how a candidate thinks and delivers value.
3. Compress interviews. Batch interviews within 48–72 hours to keep momentum high and decision-making sharp.
4. Use a clear scorecard tied to business outcomes. It reduces bias and keeps discussions objective.
5. Move with intent. Give feedback within 24 hours and communicate clearly throughout. 

Source: Case Study on Building AI Leadership from the Ground Up

The Harnham Difference

At Harnham, we’ve refined our approach through years of helping start-ups and scale-ups grow their data capability at pace.

Thorough vetting. Every candidate is assessed not just for technical ability but for adaptability and stage fit.

Specialist expertise. Our deep industry knowledge enables us to understand your unique requirements and provide candidates who can deliver immediate value.

Long-term focus. We match candidates to trajectory, ensuring each hire grows with the business.

Tailored delivery. Whether you need a single strategic hire or a full data function, we adapt to structure, timelines, and goals.

Hiring to Scale? Let’s Make it Simple.

If you need fast, high-quality data talent or want to learn more about our rapid-hire process, connect with Guillian Eller on LinkedIn. 

The post From Start-Up to Scale-Up: Hire Exceptional Data Talent in Two Weeks appeared first on Harnham.

At a glance

The Northern data and tech market is entering a new stage of maturity. Demand remains strong, but priorities are shifting from building capability to deploying it.

According to Harnham’s Northern Data & Tech Market Update (Sept 2025), organisations across the region are now competing for professionals who can deliver value at speed.

This regional shift reflects a wider national trend. The UK’s AI sector grew rapidly in 2024, with revenues rising 68 % to £23.9 billion and AI-related employment increasing 33 % to 86,000 roles. These numbers reflect how AI and data capability are no longer future plans but active economic drivers. (Source: Gov.UK)

Over the following sections, we map where demand is strongest, what’s driving it, and how leading employers and skilled professionals in the North are adapting.

1. What’s Changing in the Northern Market

The Northern data market is shifting from experimentation to execution.
AI integration has moved from pilot projects to production, and cloud modernisation remains the backbone of transformation.

Harnham’s Northern Data & Tech Market Update reveals employers now prioritise hybrid profiles: data engineers fluent in cloud migration, analysts driving CRM personalisation, and data scientists capable of deploying models into production.

For employers, this means competition has moved upstream; two-stage processes are filling roles faster, and flexibility is becoming a key differentiator alongside salary.

For candidates, opportunity lies where technical skill meets delivery readiness: the ability to build, test, and operationalise insight at speed. Professionals who can connect data to business value are the ones commanding top-tier packages and long-term career security.

2. Cloud Stack by Company Size

Harnham’s market analysis shows a clear pattern in cloud adoption across Northern firms:

• Large enterprises → GCP. Favoured for data-heavy, AI/ML-driven projects leveraging BigQuery and Vertex AI.

• Mid-sized businesses → Azure. Chosen for legacy migration and integration with existing Microsoft ecosystems.

• Smaller companies → AWS. Still the most popular for flexibility, scalability, and cost efficiency.

This split is shaping demand for data engineers experienced in migration and multi-cloud environments, particularly those who can maintain compliance and governance standards during rapid change.

3. CRM & Customer Analytics

CRM hiring is one of the fastest-growing areas in the North, especially across hospitality, retail, and financial services.

Harnham data shows a strong shift towards Braze, a platform overtaking Salesforce in new implementations due to lower cost and stronger engagement tools.

Harnham’s recent placements of Braze-focused CRM Managers in Staffordshire and Leicestershire point to a broader shift toward agile, campaign-led stacks. Alongside platform expertise, employers now prioritise SQL/Python, campaign analytics, and MMM/attribution to tie spend to outcomes.

4. AI’s Impact on Hiring: Software Meets Science

This shift towards production-ready AI talent is redefining the balance between data science and engineering teams.

Across the North, companies now want engineers who can deploy, not just develop. Skills in Kubernetes, containerisation, and CI/CD pipelines have become core to getting models from lab to live. 

The most valuable hires blend software architecture and applied ML, bridging the traditional gap between engineering and data science. Employers increasingly expect data scientists to own deployment and pipeline management, a capability now commanding £100k+ salaries.

5. Spotlight: The North East’s AI Growth Zone

The North East is fast becoming one of the UK’s most dynamic AI hubs. The forthcoming AI Growth Zone is set to create 5,000 new jobs and attract £30 billion in investment, connecting universities, AI firms, and government initiatives in a single innovation corridor.

For employers, it’s a chance to establish a presence in a region gaining national visibility for AI capability. For data professionals, it opens doors to applied research, product development, and start-up collaboration, all without relocating south.

Harnham’s analysis suggests this momentum will sustain demand over the next two years for AI engineers, MLOps specialists, and data governance experts as the region scales.

6. What’s Working in Hiring

Harnham’s Northern Data & Tech Market Update (Sept 2025) highlights three factors separating fast-moving employers from those losing ground:

1. Flexibility attracts talent
   Around one-third of Northern employers now offer four-day or compressed-week structures. Once experimental, these are now proven differentiators in both attraction and retention.
2. Speed secures hires
   Companies using two-stage interview processes achieve a 30% higher fill rate than those running three or more rounds. 
3. Clarity closes offers
   Demand for cross-functional data talent is rising, and employers who combine clear technical assessment with a fast, structured process are filling roles first.

7. What’s Next for Northern Data Hiring

The Northern market’s evolution reflects a wider UK trend: AI maturity now depends as much on people as on platforms.

For hiring leaders, that means focusing on speed, structure, and substance. Building recruitment processes that are efficient, transparent, and centred on long-term capability. Flexibility and learning culture are also fast becoming the deciding factors in attracting top data talent.

For professionals, adaptability is the new edge. Expanding skills in deployment, cloud, and communication will be key to thriving as AI becomes embedded in every data role.

As the global leader in Data & Analytics recruitment, Harnham continues to help organisations across the North build future-ready teams and to support professionals shaping the next chapter of data and AI.

Explore current roles and insights at harnham.com.

The post What’s Driving Northern UK’s Data & AI Hiring in 2025? appeared first on Harnham.

No team. No roadmap. Just a mandate to lead the charge into intelligent automation, better risk modelling, and scalable innovation.

They came to Harnham with a clear brief: find senior AI leaders who could build the plan and deliver it.

Here’s what happened and what your business can take from it.

The Brief

• Objective: Make two senior AI hires from scratch 
• Timeline: Urgent. 
• Context: No existing AI infrastructure or internal team 
• Requirement: Deep AI/ML expertise and the credibility to engage at C-suite level 

The Result

Within just 18 working days, we took the brief, identified top talent, and made two high-impact placements, delivering exactly what the client needed without wasting time or overwhelming their internal team.

The roles:

• Director of AI Strategy – Defined the AI roadmap, budgeted initiatives, aligned internal stakeholders, and evaluated vendors. 
• Director of AI Engineering – Built the architecture and models to bring that roadmap to life, from pipelines to deployment.

These were $400k+ hires based in New York, shaping the client’s internal capabilities and the digital transformation of their portfolio companies.

What Changed

These hires laid the groundwork for real change. With leadership in place, the client could start applying AI where it mattered: automating manual processes, improving how risk is modelled, and identifying new ways to support their portfolio companies.

Midway through the process, the brief evolved. One role became two. We adapted quickly, and the result is a structure where strategy and delivery are clearly defined; with one leader now managing the other.

It’s a setup designed not just to scale, but to last.

3 Lessons for AI Hiring Leaders

1. Start with leadership
   Engineers alone can’t set vision, structure governance, or align the board.
2. Match your sector
   This client operates in private equity, which means they couldn’t rely on generic AI talent. They needed leaders who understood financial services, regulatory requirements, and how to drive long-term value across a portfolio.
3. Hire for impact
   We didn’t flood them with profiles. We delivered 14 targeted CVs and filled both roles in under 4 weeks.

The Bigger Picture

You’re not alone if you haven’t got it all figured out.

According to Harnham’s internal research, 61% of professionals already use AI in their roles but just 37% of companies have a policy in place. (Source: Harnham AI Report – April 2025)

That’s a big risk especially in regulated industries like finance. The good news? The right hire can change that.

Want the full story?

Download the case study and discover how Harnham delivered senior AI leadership in under three weeks and what your business can learn from it.

Hiring for AI? Let’s Make It Simple

Download our AI Hiring Guide – a practical resource to help you identify which roles to hire and when.

Or book a consultation with an AI recruitment expert who understands your industry.

The post From Zero to AI: How One Investment Firm Built a World-Class Team in 18 Days appeared first on Harnham.

By Roshni Baillie, Recruitment Consultant – Marketing & Insight, Harnham

One thing I’m hearing again and again lately is the question: “Is AI going to replace analysts?”

At Harnham, I have a front row seat to the AI revolution, and to how companies and candidates are responding. While AI is having a clear impact on everyday life, from ChatGPT recipe recommendations to iPhone message summaries, some companies are still behind the curve when it comes to applying these advancements to their data. Once a buzzword, AI has become the engine driving how businesses think, plan, and grow. From predictive forecasting to real-time decision-making, AI is transforming analytics from dashboards and SQL queries into something far more powerful.

Working day-to-day with Marketing & Insight professionals, I’m seeing an ever-growing trend: quality candidates favouring innovation over benefits. The days of “just reporting numbers” are behind us – top talent want the opportunity to experiment, innovate, and drive change. Time and time again, I hear frustration from those stuck with outdated tools and mindsets, knowing they could deliver far more if given the space and support to do it.

From retrospective reporting to predictive and prescriptive insights

While traditional analytics focused on what happened, utilising AI allows teams to ask what should happen next and what should be done about it. Businesses can now forecast customer churn, demand fluctuations, and supply chain risks with unprecedented accuracy.

More importantly, prescriptive analytics can recommend specific actions and data-led solutions rather than just surfacing problems. I’ve found that building a team of analysts with a strong understanding of algorithms, not just dashboards, is game-changing. Talent with a handle on predictive modelling, time series forecasting, and tools such as Python, R, and AutoML platforms are in high demand – and they’re raring to go.

From static dashboards to real-time decision-making

In today’s landscape, BAU and end-of-week reports aren’t enough. Businesses want insights delivered as they happen, acting as the data lands to keep one step ahead.

In Marketing & Insight, that might look like campaign performance being adjusted mid-flight, or personalisation engines updating in real-time based on customer behaviour. AI is making this possible, whether it’s fraud detection models flagging anomalies in milliseconds, or ops teams rerouting deliveries based on live traffic data.

Static BI tools are no longer enough. Data professionals comfortable with stream processing, APIs, and real-time tools such as Kafka, Snowflake, and Spark Streaming can deliver insights that prevent issues before they even surface.

From gut feel to AI-enhanced strategies

AI is far more than a backend tool, it’s a major player in frontline strategy. Natural language processing and generative AI can surface trends from millions of customer reviews, summarise complex data into digestible insights for technical and non-technical stakeholders, and help marketing teams test thousands of ad variations in minutes.

For me, this is where soft skills matter now more than ever. The ability to translate AI insights into business action to tell the story behind the data is what separates good analysts from great ones.

The hiring takeaway

AI is changing how businesses maximise the value of their data, but it’s also changing what the best data professionals expect from a role.

If you’re looking to build a high-performing analytics function in 2025 and beyond, your hiring strategy needs to reflect that. Innovative, forward-thinking talent will help you get the most out of your data, giving it more value than ever before.

The future of analytics is intelligent and AI-driven and it takes a strong, well-equipped team to get you there. If you’re hiring in Marketing & Insight and want to explore how to bring this talent into your business, I’m always happy to share what I’m seeing in the market.

Hiring for AI? Let’s Make It Simple

Download our AI Hiring Guide – a practical resource to help you identify which roles to hire and when.

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  By David Farmer, CEO, Harnham Global

We haven’t just hit 15,000 placements, we’ve launched 15,000 careers, changed 15,000 lives and it’s a marker. A marker of how far the Data & AI industry has come and what it’s 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’ve been on this journey with the industry since the beginning.

We’ve 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 ‘Data person’ role, required to wear multiple hats (analysts, developers, engineers), to more specialised roles with defined purpose such as ‘Data Engineers’ ‘BI Analysts’ and ‘AI engineers’. 

Now, we’re sharing intel about what we’ve learned and where we think it’s all going next.

What’s Changed?

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 “need” 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.

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’s MPhil in Ethics of AI, Data and Algorithms showing what we believe to be a necessary direction of travel. 

Day to day, Harnham started out by placing analysts who could build dashboards and run reports. Now, we’re 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’s a shift from siloed analytics to fully embedded teams, and from back-office reporting to real-time, business-critical decisions.

In further articles we will focus in on individual roles, responsibilities and titles to highlight the shift seen within each part of the industry.  

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.

What Hasn’t Changed?

One thing that’s remained consistent: the ability to take what the data is saying and tell someone else about it in a way that matters. That’s always been the difference-maker. It’s never just been about the code or the model – it’s 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. 

We will be covering this in a whitepaper, which will be available soon.

Yes, there’s 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.

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.

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. 

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. 

Overall though, these 15,000 candidates don’t 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.

For Candidates: The Shifting Landscape

• It’s not about having the perfect CV anymore. It’s about showing you can learn, adapt, and apply.
• People who started as SAS programmers are now AI Architects. That shift didn’t happen overnight, and it didn’t happen by accident.
• Lifelong learning isn’t a buzzword. It’s the reality. Be prepared to talk about your engagement in learning.  
• The average CDO will want to see actual demonstrations of work outside of your CV. If you’re a tableau analyst it is important not just to show dashboards you have built, but just as important to talk about what the outcome from those dashboards was.

For Businesses: Solutions Over Tools

• The best companies start with the problem, not the job title. They figure out what they need to solve and then hire someone to do that.
• Hiring in layers works. We’ve seen it — data engineers, AI leads, and governance heads.
• Governance isn’t optional anymore. Neither is training. Whether it’s upskilling your own teams or bringing in early-career consultants, you have to be thinking long-term.

What’s Next?

To celebrate 15,000 placements, we’ll be sharing a range of insights and interviews with clients, candidates and others from our Harnham network over the years, including a live panel discussion on what’s next for the industry. Watch this space.

Check out our 15,000 placements exclusive podcast:

The Data & AI Podcast: 15,000th Placement Exclusive with Charlie Nicholls from Dexata: LISTEN HERE

• What it takes to get personalisation right at scale
• How AI is reshaping the workplace and decision-making
• The role of experimentation in creating human-centric experiences
• Why customer experience optimisation is more critical now

Are you ready for AI?

In a landscape dominated by discussion of AI, companies can find it hard to navigate the marketplace.

Download our “AI Hiring How-to Guide” for insights on key roles, salaries and more:

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Job opportunities in permanent and contract roles are set to rise, driven by investments in emerging technologies and responsible AI development. Coupled with initiatives in upskilling and ethical frameworks, the sector is poised to overcome challenges while fostering sustainable growth.

For organisations, the balance between seizing opportunities and navigating rising employment costs will shape hiring strategies. Professionals, meanwhile, must stay ahead of industry demands to capitalise on emerging roles in AI, compliance, and data governance.

Key Budgetary Changes Affecting Employment in Data and AI

The budget outlines over £100 billion in capital investment over five years, with a significant portion focused on R&D in emerging technologies. Initiatives like ‘Skills England’ backed by an additional £300 million funding for education and apprenticeships, aim to close the digital skills gap, creating a workforce ready to meet the growing demand for data-centric roles.

Impact on Permanent Employment: Expanding Opportunities

As digital transformation remains a priority, significant funding is driving long-term opportunities for permanent employees. Public sector projects, in particular, are incentivising organisations to hire for roles that manage complex, ongoing data initiatives. This commitment bolsters job security and enhances the public sector’s ability to deliver on digital transformation goals.

Increased funding for public sector projects supports organisations in hiring permanent employees to lead ongoing digital transformation efforts. This approach strengthens long-term project management and improves job security for data professionals as they take on critical roles within large-scale initiatives.

The additional funding includes initiatives such as the ‘Growth and Skills Levy,’ which supports apprenticeship reforms and encourages organisations to invest in workforce development.

Organisations like Rockborne, Harnham’s graduate development arm, are instrumental in bridging this skills gap. Through their bespoke training programmes, including AI and Prompt Engineering, Rockborne equips graduates with the specialised skills needed to thrive in data-driven fields. 

Businesses can access a broader, highly trained talent pool ready to take on advanced data roles, potentially increasing the appeal of permanent positions within the sector.

Impact on Contract Employment: Shifts and Challenges

With the series of legislative and financial changes in 2024, organisations are now forced to face new considerations when hiring contract-based data and AI professionals. The upcoming increase in employer National Insurance contributions (from 13.8% to 15%) and the reduction in the NIC threshold to £5,000 could lead employers to re-evaluate the balance between permanent and contract roles to manage costs effectively​. 

For many, contract-based solutions are becoming an attractive way to access specialist skills while remaining agile in the face of evolving compliance demands.

Dave Curtis, Global Managing Director of Contract at Harnham, explains:

“While employers continue to face rising costs, many are likely to emphasize a more flexible workforce solution. This will potentially include increased reliance on contractors, freelancers, and automation to manage economic pressures while maintaining operational agility.”

At the same time, the Employment Rights Bill is introducing significant updates that are reshaping hiring strategies. New protections, such as the “day one” right to claim unfair dismissal, encourage organisations to adopt more cautious approaches to permanent hiring.

Adding further complexity, the Bill also enforces stricter PAYE requirements for umbrella companies, placing additional administrative responsibilities on recruitment agencies and client businesses. This combination of cost pressures and compliance challenges is driving some organisations to prioritise contract roles as a flexible and efficient means of meeting project-specific needs.

Looking Ahead to 2025: Key Trends and Predictions

As 2025 approaches, hiring in Data and AI will be shaped by the interplay of budgetary investments and evolving legislation. 

Contract roles will dominate short-term, high-impact projects, with companies seeking specialised expertise in AI ethics, machine learning, and compliance. Meanwhile, new regulations like PAYE compliance for umbrella companies and higher employer NIC contributions will make hiring more complex, potentially driving a hybrid workforce model that balances flexibility with stability.

Businesses that adapt quickly to these changes will be best positioned to optimise strategies, mitigate compliance risks, and attract top talent. For professionals, staying informed and agile will be critical to seizing opportunities in this growing sector.

Conclusion

The Autumn Budget 2024 and legislative changes are pivotal for Data and AI recruitment. Companies must adapt to rising costs, compliance demands, and shifts in workforce models to stay competitive. Similarly, professionals should embrace upskilling opportunities to align with evolving industry demands.

For further insights and tailored guidance, visit Harnham’s industry hub. 

Explore the latest trends and expert advice to help you navigate the future of Data and AI recruitment with confidence.

Learn more about our Data & AI talent solutions HERE.

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For example, Ross Henderson, Director and Head of Harnham’s Netherlands branch, has noticed a growing concern among hiring managers and companies to find “the right fit,” and as a result, many have started to add an extra “surprise” interview stage at the end of their recruitment process, after a candidate has completed the specified interviews they were initially prepared for.

While it’s understandable that companies and hiring managers feel the need to ensure their new hires are a good match, Henderson explains why adding an extra interview stage at the very end of a hiring process can be detrimental, and may actually deter top-tier talent from joining your team.

Three Reasons Why You Shouldn’t Add an Extra Interview

Below, we share three reasons why adding an extra “surprise” interview can be a risky move, and what you can do instead to gauge a candidate’s fit without putting them off the role.

Reason 1: Increased chance of losing the candidate to competition

The longer you drag out an interview process, the more opportunity you are giving your competitor to approach your interviewee. The best candidates are typically being approached about multiple roles, and the time added to your hiring process gives more time for other businesses to complete their process, sell their roles, and make offers.

Reason 2: Decreased candidate experience

Candidates often make assumptions about the reason for adding an extra stage. They may come to the conclusion that your company is disorganised, that you aren’t sure on their application or their fit, that you’re holding out for a better candidate, that their expertise is being used for consultancy experience, or the company isn’t serious about the hire. Often, these are unfounded, but based on conversations with candidates, these things come up and can taint the candidates experience and their perception of your organisation.

Reason 3: 50% of candidates said it “puts them off” of a role

We turned to our network of thousands of data professionals on LinkedIn, and ask them via a LinkedIn poll how they felt about an extra stage being added to an interview process after completing the number of interviews specified at the beginning—of the 1,300 respondents, only 5% said they liked it, while 78% said they either felt put off by it or hated it.

As the data shows, there’s a very high chance that adding an extra interview will give a candidate a negative impression of your organisation, and the reality is there are better ways to thoroughly screen a candidate to ensure the right fit.

Ross’ Recommendations

Of course, there are scenarios where an additional interview is unavoidable. However, there are steps you can take to ensure your candidate’s experience is as smooth and streamlined as possible:

• Really know what you’re looking for: have a clear picture at the start of the process on the competencies, technical skills, and culture requirements you’re looking for. Identify which skills are essential, and which ones are just nice to have. You can do this by putting them into a framework to assess them. This is an important step because it can be easy to get excited by a candidate, only to realise down the line that you hadn’t tested them on several things that are crucial for the role.
• Map out your process beforehand: Figure out how you’re going to test your candidates on the core competencies of the role, ideally in three steps or fewer. Be clear about what needs to be tested and when.
• Loop in the right stakeholders early on: When mapping out your process and defining core competencies, loop in the budget holder, the HR / Talent team, and a specialist recruitment partner. If everyone aligns at this stage, it is less likely to change during the process.

It’s completely understandable that requirements can change during a process and interviewing candidates can sometimes make a manager realise what they need. If that does happen during processes, don’t be afraid to iterate and redefine the process for the new requirements. Proactive communication, and context for candidates, is key.

If your company needs support finding the right data talent, get in touch with our team today.

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