Head of Data Science & Analytics

Harnham
London
3 days ago
Create job alert

Head of Data Science & Analytics

London or Manchester (2 days per week in office)

Up to £120,000


I’m hiring for a Head of Data Science & Analytics to lead a dynamic and growing data function within a global media organisation. With a strong digital presence and a wide-ranging portfolio of consumer brands across publishing, audio, and advertising, this is a brilliant opportunity to shape the future of data strategy across the UK and European markets.


Why this role?

You’ll be joining at a pivotal moment as the business strengthens its audience intelligence, inventory analytics, and digital engagement capabilities. This role offers significant ownership — leading a cross-functional team, working on high-impact projects, and driving real commercial and audience insight across multiple countries.


Key Focus Areas:

Inventory Optimisation – Help the business unlock better pricing strategies and revenue forecasting for advertising space

Audience Analytics – Understand user behaviour and engagement across platforms to support smarter content and programming decisions

Competitions & Promotions – Drive targeting strategies for high-volume competition campaigns and optimise performance using predictive models


Your Mission:

  • Lead and scale a high-performing data science and analytics team
  • Design and implement predictive models to drive decision-making across business verticals
  • Champion the use of data science, product analytics, and A/B testing across commercial and content teams
  • Build scalable data solutions that align with both UK and European markets
  • Act as a strategic partner to senior stakeholders across digital, commercial, and audience functions
  • Foster a culture of innovation, curiosity, and insight-led thinking


What We're Looking For:

  • Proven experience in a data leadership role – ideally managing multidisciplinary teams (data science, BI, analytics, etc.)
  • Strong technical foundation – hands-on with SQL, modelling, experimentation and product analytics
  • Strong communication and stakeholder engagement skills – able to influence non-technical teams and shape strategic direction
  • Sector experience is flexible, though backgrounds in media, advertising, digital platforms, or high-engagement B2C environments are especially valued
  • For certain areas (e.g. competitions), experience in gaming or gambling analytics could be a strong plus
  • Ultimately, we’re after someone collaborative, commercially-minded, and passionate about making data work for people


📩 Interested in learning more or know someone who fits the bill? Send your CV to or through the Apply link on this page



#Hiring #DataScienceJobs #HeadOfData #AnalyticsLeadership #MediaCareers #ProductAnalytics #InventoryAnalytics #AudienceIntelligence #TechLeadership

Related Jobs

View all jobs

Head of Data Science and Analytics

Head of Data Science and Analytics

Head of Data Science and Analytics

Head of Data Science and Analytics

Head of Data Science and Analytics

Head of Analytics & Data Science

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.