Head of Data and Analytics

Harnham
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Head of Data Analytics

Head of Data Analytics & AI

Head of Data Architecture

Head of Data Architecture

Senior Claims Data Analyst

Head of Data & Analytics – Strategy Consulting

Location: London, Victoria (3 days in office per week)

Salary: Up to £125,000


Please note this role is not able to offer sponsorship now or in the future.


An established strategy consulting firm with a global presence across the UK, LATAM, and Asia is seeking an experienced Head of Data & Analytics to lead their core services team. The organisation prides itself on delivering innovative, data-driven solutions within a collaborative corporate environment.


This is a non-technical, client-facing leadership role, overseeing a team of 20 senior professionals specialising in Data Science, Data Engineering, and Analytics.


The Role and Opportunity

In this role as a Head of Data & Analytics, you will:

  • Provide leadership and direction to a high-performing team, managing Associate Directors and senior specialists across data disciplines.
  • Act as the primary client interface, defining requirements and ensuring successful delivery of tailored data solutions.
  • Collaborate with clients and stakeholders to align technical outputs with strategic business goals.
  • Support cross-functional collaboration, working closely with teams across multiple disciplines.
  • Champion best practices and foster a culture of innovation and excellence within the team.


Key Requirements

To qualify for this role, you will require:

  • Extensive experience in strategy consulting, including leading cross-functional teams.
  • Strong understanding of data analytics or data science, with a technical background.
  • Proven track record of managing senior stakeholders and high-performing professionals.
  • Exceptional communication and leadership skills, with the ability to translate complex technical ideas into actionable business strategies.


How to Apply

Please apply via the Apply link on this website.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.