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.

Data & Analytics Hiring Trends

How working models, technology choices, and assessments are changing hiring

These data and analytics hiring trends reflect how working patterns, technology choices, and assessment methods are changing across analytics roles, based primarily on hiring activity with Northern-based clients.

While many of these trends are not exclusive to the North, they reflect patterns we are consistently seeing across organisations hiring data and analytics talent in Northern England.

Let’s take a look at the key shifts influencing hiring decisions today, from hybrid working and low-code tools to Microsoft Fabric, marketing mix modelling, and technical assessments.

On this page

• • Hybrid working expectations in data and analytics roles

• • Low-code platforms in analytics teams
  • Microsoft Fabric skills and hiring considerations
  • Why marketing mix modelling is becoming a core analytics skill
  • Why technical assessments are moving earlier in hiring processes
  • How Harnham can support hiring decisions

Hybrid working expectations in data and analytics roles

Hybrid working is no longer a differentiator for many data and analytics roles. For a significant share of professionals, some degree of flexibility is now expected as part of the role.

Across much of the market, candidates commonly look for one to two days per week in the office. While some larger organisations, particularly in banking and retail, continue to push for higher on-site attendance, this tends to narrow the available talent pool rather than expand it.

In practice, expectations vary by sector and region. For example, we are currently supporting a retailer in the Midlands and a financial services organisation in the Midlands that are both seeking candidates to be on site four to five days per week.

These models are typically more achievable for large, established employers with strong brand recognition, but they tend to reduce the available talent pool and extend hiring timelines compared to more flexible approaches.

Stricter office requirements are often associated with:

• Reduced access to candidates who have relocated or prioritise flexibility
• Longer hiring timelines as talent pools become more constrained
• Increased salary pressure to offset reduced flexibility
• Greater reliance on relocation packages or regionally limited hiring strategies

Hybrid working expectations now play a material role in hiring outcomes for data and analytics teams. 

Employers with more rigid attendance requirements often need to compensate through pay, brand strength, or the nature of the work itself.

Low-code platforms in analytics teams

Low-code platforms are increasingly used in analytics and finance teams to accelerate delivery where engineering capacity is limited. In the short term, this can reduce dependency on specialist roles and improve speed to insight. Over time, the trade-offs become more visible.

We are seeing particularly strong uptake of low-code tools within financial services organisations, including a number of clients based in Leeds and Birmingham. In these environments, low-code platforms are often used to accelerate reporting and analytics delivery while managing engineering capacity and regulatory constraints.

Initial benefits typically include faster development cycles, broader accessibility for analysts, and lower upfront costs through built-in governance and security controls.

As adoption scales, teams often encounter:

• Reduced flexibility for complex or bespoke use cases
• Licensing costs that increase as usage expands
• A gradual erosion of in-house engineering capability
• Longer-term skills gaps that surface as platforms are pushed beyond their original scope

Decision point
Low-code tools can support near-term delivery goals, but hiring decisions need to account for long-term capability, maintainability, and technical depth.

Microsoft Fabric skills and hiring considerations

Microsoft Fabric is gaining traction primarily in organisations already operating within the Microsoft ecosystem. Hiring demand is less about the platform itself and more about whether teams can build capability without introducing cost or delivery risk.

We are increasingly seeing Microsoft Fabric referenced in role requirements across a range of industries. This is particularly noticeable in established data hubs such as Manchester, Cheshire, and Leeds, where organisations are building on existing Microsoft-based data estates and looking to consolidate analytics capability.

Interest is typically driven by the promise of a unified OneLake architecture, close integration with Power BI and Azure, and the continued expansion of AI-enabled features across the platform.

At the same time, employers frequently raise concerns around:

• Cost visibility and predictability at scale
• Platform maturity and the pace of product change
• The training investment required to build reliable capability

As a result, many roles reference:

• Power BI and Fabric experience
• OneLake architecture knowledge
• DP-600 and DP-700 certifications, often alongside demonstrable hands-on delivery

Fabric adoption is increasing, but hiring decisions are shaped as much by risk management and cost control as by platform capability.

Why marketing mix modelling is becoming a core analytics skill

Marketing mix modelling is appearing more frequently outside specialist teams, particularly in organisations adjusting to reduced access to user-level data.

Data & AI Podcast: How Incrementality and MMM Are Changing Marketing Analytics

Rather than replacing attribution entirely, MMM is increasingly used alongside other approaches to support decision-making where privacy constraints limit traditional measurement methods.

Demand is being shaped by:

• Cookie deprecation and iOS privacy changes
• GDPR and CCPA restrictions on data use
• A need for privacy-compliant ways to assess marketing effectiveness

From a commercial perspective, MMM supports:

• Marketing ROI analysis without reliance on personal data
• More disciplined allocation of large marketing budgets
• Decision-making that holds up under regulatory scrutiny
  

Marketing mix modelling is increasingly treated as a core analytics capability rather than a niche specialism, and is appearing more often in standard marketing analytics roles.

Why technical assessments are moving earlier in hiring processes

As hiring teams place greater emphasis on validating practical capability, technical assessments are being introduced earlier in the interview process. This shift is partly influenced by wider use of AI tools, but more directly by the cost of late-stage hiring mistakes.

Assessment formats increasingly reflect real working scenarios rather than theoretical exercises.

Typical examples include:

• Data analysts and analytics engineers
  Live SQL tasks, Excel or Python exercises, dashboard interpretation
  
• Data scientists
  Real-time coding, statistical reasoning, model evaluation, live notebook sessions
  
• Marketing analytics and MMM roles
  Regression interpretation, p-values, causation versus correlation
  
• BI and visualisation roles
  Dashboard critique and stakeholder scenario discussions

As hiring timelines tighten, many organisations are also reassessing how capability is resourced. In AI-focused teams, this has led to greater use of flexible delivery models, with some US companies scaling AI capability through contractors to meet delivery goals without adding long-term headcount risk.

Related read: Why US Companies are Scaling with Contractors

Earlier technical assessments allow teams to validate capability sooner, reduce late-stage risk, and make hiring decisions with greater confidence.

How Harnham can support hiring decisions

Across data and analytics teams, hiring challenges are rarely about volume alone. More often, they come down to how roles are defined, how teams are structured, and how capability needs to evolve as priorities change.

Harnham works with organisations and professionals across the full data and analytics lifecycle, supporting hiring decisions that reflect current market conditions rather than assumptions.

If you’d like to explore how these trends translate into team structure, role design, and delivery outcomes, you can learn more about our Data and AI Talent Solutions, covering permanent, contract, and project-based hiring.

Contact Harnham to discuss your data and analytics recruitment needs.

The post Data & Analytics Hiring Trends: Market Insight appeared first on Harnham.

Analytics teams support value creation in growth-stage portfolio companies by improving revenue quality, margins, cash flow, and decision discipline. For private equity and venture capital firms operating in a higher-interest, lower-multiple environment, analytics is now a core input into value creation planning rather than a supporting capability.

This article explains where analytics contributes most directly to commercial outcomes, why progress often stalls, and how operating models and talent choices influence results.

On this page:

• • Why analytics matters now for private equity portfolios
  • What value creation through analytics looks like in practice
  • How AI and analytics operating models affect portfolio-level value
  • Why analytics initiatives stall in portfolio companies
  • How stronger analytics teams overcome these issues
  • How to structure analytics teams for value creation
  • What this means for operating partners and investors

Why analytics matters now for private equity portfolios

Value creation has shifted inward. Longer hold periods, higher financing costs, and closer scrutiny of forecasts mean performance must be supported by stronger internal controls and clearer visibility into how the business operates.

Recent survey data underscores the pressure. In the North America Value Creation in Private Equity Report 2025 from Alvarez & Marsal, only 31% of respondents reported a positive outlook on deal activity over the next 12 months. 72% realized less than 75% of planned value, and 55% are now investing in value creation initiatives more than one year into the hold cycle.

In this environment, analytics is increasingly used to:

• Improve confidence in revenue and margin forecasts
• Identify operational inefficiencies earlier
• Support pricing, cost, and working capital decisions
• Strengthen exit narratives with evidence rather than assumption

Across private equity research, a consistent pattern emerges: analytics contributes most when ownership is clear, priorities are commercially defined, and teams are positioned close to the decisions that affect revenue, cost, and cash flow. 

What value creation through analytics looks like in practice

Revenue quality and pricing discipline

Analytics supports revenue performance by improving the quality and consistency of commercial decisions, rather than driving volume alone. In PE-backed businesses, this most often shows up in areas such as:

• Customer and product segmentation
• Pricing visibility and discount governance
• Regional or channel-level sales performance analysis

Where analytics capability is positioned close to commercial leadership, these approaches help reduce decision variability and support more disciplined margin management over the hold period.

Cost and margin control

Operational analytics often contributes early to margin improvement because it focuses on reducing variability rather than changing behavior at scale. Typical use cases include:

• Predictive maintenance in asset-heavy environments
• Demand and capacity forecasting
• Automation of repeatable finance and operational processes

These initiatives tend to be tied to clearly defined cost drivers, which makes outcomes easier to track and manage.

Working capital efficiency

For capital-intensive portfolio companies, analytics frequently delivers value through improved cash management. Common use cases include:

• Inventory optimization
• Forecasting accuracy improvements
• Reductions in excess stock or expedited procurement

These initiatives tend to be easier to govern and measure than broader transformation programs because they are directly linked to cash flow and operational efficiency.

Data monetization, where appropriate

Data monetization is not relevant to every portfolio company. Where it does apply, it typically follows earlier investment in data quality and operational analytics. Examples include:

• Benchmarking products
• Embedded customer insight services
• Data-led product extensions

This type of value creation tends to emerge later, once core reporting and decision support are stable.

How AI and analytics operating models affect portfolio-level value

For operating partners and private equity leadership, one of the most consequential analytics decisions is not technical but structural: who owns analytics, and at what level.

Research from FTI Consulting identifies four common operating models, defined by the degree of centralization across the portfolio:

• Decentralized: each portfolio company owns analytics independently
• Center of Excellence (CoE): one or more portfolio companies act as capability hubs
• Fund-specific: shared analytics capability across a subset of assets
• Centralized: firm-level ownership of policy, priorities, and platforms

Portfolio-level value creation depends on how effectively knowledge, talent, and repeatable use cases can be shared across assets. Firms that move incrementally toward greater centralization, particularly around policy, prioritization, and architecture, are better positioned to reuse what works, rather than rebuilding analytics capability asset by asset.

Why analytics initiatives stall in portfolio companies

Many PE firms are asking, “What’s the right way to use AI in value creation?”

It’s one of the most controversial questions in private equity. Not because AI lacks potential, but because too many initiatives start with use cases rather than readiness. More often, it is timing, leadership, and alignment with the value creation plan.

Common constraints include:

• Fragmented systems and inconsistent data definitions
• Legacy infrastructure that limits integration
• Teams positioned too far from commercial decision-makers
• Lack of senior ownership for outcomes

As Gavin Geminder, Global Head of Private Equity at KPMG, notes:

“Having clear, ethical AI guidelines in place is going to build employee trust and customer satisfaction, while also enhancing GPs’ brands.”

In FTI Consulting’s AI Radar for Private Equity 2025, 36% of PE firms with an AI strategy reported having no specific milestones or KPIs to measure impact on value creation. Without clear ownership, success measures, or prioritization discipline, initiatives tend to accumulate as pilots rather than translate into sustained operational change.

How stronger analytics teams overcome these issues

High-performing portfolio companies take a deliberate, value-led approach.

Focus on defined, near-term use cases

Initiatives are selected based on expected commercial impact within the first 6–12 months, aligned to the investment thesis.

Embed analytics into commercial and operational teams

Analytics works alongside sales, operations, and finance, with shared accountability for outcomes rather than downstream reporting.

Align management, operating partners, and investors

Priorities are reviewed regularly to ensure analytics remain tied to the value creation plan as the business scales or changes direction.

How to structure analytics teams for value creation

Team structure and leadership choices play a significant role in whether analytics contributes to value creation.

In growth-stage portfolio companies:

• Analytics leadership often reports into the CFO or COO initially
• As scope increases, responsibility may move to a dedicated Head of Analytics or Chief Data Officer with board-level exposure

Sequencing matters more than team size. In many cases:

1. A commercially credible analytics lead is hired first
2. Data engineering capability is added to improve reliability and scale
3. Applied data science is introduced where specific use cases justify it

Hiring technical depth without sufficient commercial context is a common cause of slow progress. Many PE-backed businesses use market benchmarks, such as Harnham’s Data & AI Hiring Guide, to sense-check seniority, expectations, and retention risk through the hold period.

Source: Harnham’s How to Hire in AI

What this means for operating partners and investors

Analytics should be treated as part of the value creation plan, instead of a standalone capability.

Useful questions to ask portfolio leadership teams include:

• Which commercial decisions does analytics directly support?
• Who is accountable for outcomes, not just reporting?
• How does analytics align with the investment thesis and exit plan?

When these questions are addressed early, analytics is more likely to support sustained performance improvement.

Analytics value creation quick reference

How Harnham supports analytics-driven value creation

Across private equity portfolios, the real challenge is how teams are structured, led, and scaled in line with the value creation plan.

Harnham supports private equity and venture capital firms by helping define analytics leadership requirements, assess team structure, and benchmark roles as portfolio needs evolve. Our work focuses on hiring decisions that support commercial priorities, operating discipline, and long-term exit readiness.

For firms reviewing analytics leadership or team structure across portfolios, you can explore Harnham’s analytics hiring capabilities here or get in touch for a market-led discussion.

The post How Analytics Teams Drive Value Creation in Growth-Stage Portfolio Companies 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.

The post How a Leading European Media Group Unified Its Data Strategy with Harnham appeared first on Harnham.

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.

By Luc Simpson-Kent, Managing Consultant – Harnham US

Candidates want growth. Employers want retention.

And in today’s market, both goals rely on the same thing: continuous learning.

The pace of change in data and AI means even the most experienced professionals are going back to school, literally and figuratively.

Across the US, data scientists, engineers and analysts are reskilling faster than ever, not just to keep up with new technologies, but to stay relevant in an industry where roles evolve as quickly as the models they build.

The rise of the always-learning data professional

In Harnham’s latest US Data & AI Salary Guide, a lack of career progression ranked among the top three reasons professionals consider leaving their roles. It’s a clear signal: growth is non-negotiable.

At the same time, 67 % of respondents said they view AI positively, and nearly two-thirds already use it in their daily work. That shift isn’t just changing what people do, it’s redefining how they learn.

Many professionals are now stacking certifications across cloud architecture, data engineering, and AI/ML tools to strengthen career mobility.

Common routes include:

• Databricks, AWS and Azure certifications for cloud data infrastructure. 
• Coursera and LinkedIn Learning bootcamps for applied machine learning and Python. 
• Specialist courses in GenAI, data governance and AI ethics 

Download the Data & AI Salary Guide here.

Learning isn’t just about tools, it’s about trust

AI has pushed many companies to rethink how they train and manage their people.

Accenture research found that nearly half of all working hours in the US are now in scope for automation or AI support. In that context, professionals who can demonstrate adaptability, not just technical ability, are becoming indispensable.

It’s why communication, stakeholder management, and data storytelling are increasingly appearing alongside hard skills in job specs. Upskilling now means learning to translate insights into decisions, not just code into outputs.

AI is reshaping learning itself

What’s interesting is how AI isn’t just the subject of learning; it’s also the teacher.
Modern platforms use AI to map existing skills, spot adjacent strengths, and recommend courses that fill real gaps.

That’s turning training into something personalised and proactive, not reactive.
Instead of waiting for a promotion or performance review, data professionals can see exactly where their next opportunity lies and move toward it before the business even asks.

“Skills-based organizations are 107% more likely to place talent effectively, 52% more likely to innovate, and 57% more likely to anticipate change and respond effectively and efficiently.” (Source: Deloitte)

For employers, this precision matters. Skills-based learning data helps them redeploy talent, plan for automation, and retain people who want to grow without leaving.

It’s no coincidence that companies investing in development see stronger loyalty.

How employers can build a learning-led culture

When learning is relevant, practical, and supported, it becomes part of how teams operate

Start with business goals.
Connect learning to real projects and outcomes so employees see how new skills drive value day to day.

Adopt a skills-first mindset.
Focus on what people can do, not just their job title. Map learning around tasks, capabilities, and future needs.

Make learning hands-on.
Combine expert-led sessions with real projects so people learn by doing.

Personalise the path.
Use AI tools and internal data to tailor learning to each person’s strengths, gaps, and career goals.

Reward and recognise growth.
Celebrate progress through certifications, applied learning, and visible impact on team performance.

Support continuous learning.
Offer post-training resources and peer sessions to keep knowledge fresh and encourage collaboration.

Moving Forward

The best data professionals are already building the skills the market hasn’t even asked for yet.
And the employers that retain them are making sure those skills are built inside their own teams.

Connect with Luc Simpson-Kent for insights on how US businesses can attract, develop, and retain world-class data and AI talent.

Contact Harnham to explore talent development and workforce strategy solutions.

The post Reskilling & Upskilling: What US Data Professionals Are Doing 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.

Every company wants to build world-class AI capabilities. The challenge is that technical brilliance alone is not enough. The strongest AI teams are those that combine elite skills with diverse perspectives. Without diversity, the risk of bias multiplies and the value created by AI is capped.

Why Diversity Matters in AI

AI systems are only as strong as the data and the people behind them. Large Language Models (LLMs) are trained on datasets that reflect human bias, and those biases are amplified in production. When you add Agentic AI, multi-agent systems that make autonomous decisions, the consequences of bias become even more significant.

Without diversity in the teams designing, testing, and governing these systems, blind spots emerge. And blind spots in AI are not just ethical issues. They are commercial risks. A single oversight can mean reputational damage, compliance failures, or missed opportunities in key markets.

Moving Beyond Culture Fit

Traditional hiring models often over-emphasise culture fit by looking for people who mirror existing teams and values. In AI, this approach is not just outdated, it is a hindrance. Teams built on sameness will inevitably replicate the same biases in their models.

It is the responsibility of Data and AI leaders to create a culture that does not reward conformity but instead maximises diversity of thought, background, and approach. The best AI cultures are ones where differences are not only accepted but actively harnessed to challenge assumptions, stress-test models, and surface new ideas.

Why Automated Screening is Not Enough

Many organisations use HR tech or AI-driven screening tools to streamline hiring. While these tools have a role to play, they often work by pattern-matching against historical data or keywords. That means they are optimised for similarity rather than difference.

In the context of AI teams, this is risky. If your hiring pipeline filters for candidates who look like those already in the organisation, you reinforce sameness and overlook the very diversity that makes AI models stronger.

It is important to remember that LLMs and AI systems themselves are shaped by the diversity of their training data. The same principle applies to the teams building them. Screening out diverse backgrounds and perspectives may feel efficient in the short term, but in the long term it limits innovation and amplifies bias.

This is why leaders in Data and AI must ensure their hiring process goes beyond automated filtering. Diversity is not a hurdle to overcome. It is the foundation for building AI that is both commercially valuable and socially responsible.

What Diverse AI Teams Bring

Diversity in AI teams is not limited to demographics. It spans experience, discipline, and perspective. Strong AI teams bring together:
Researchers advancing the frontier of LLMs and generative AI
Engineers building and scaling infrastructure
Governance and compliance specialists ensuring responsible innovation
Voices from diverse cultural and professional backgrounds who challenge assumptions and spot hidden risks

This mix produces systems that are not only more accurate and reliable, but also more innovative and commercially valuable.

The Bottom Line

Diversity is not a tick-box exercise. It is the single most important factor in building AI teams that are resilient, innovative, and commercially impactful. Homogenous teams replicate bias and limit potential. Diverse teams spot blind spots, challenge assumptions, and create models that perform better in the real world.

In AI, the strongest competitive advantage is not just better technology. It is better teams, built with diversity at their core.

Need Support Building Your AI Team?

Download our AI Hiring Guide — with practical advice on where to start, which roles to prioritise, and how to structure contract and permanent hiring around real business goals.

Or book a consultation with one of our hiring specialists.

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