Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Group Head of Data Science

Harnham
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Group Head of Data Science

Global Head of Data Science

Global Head of Data Science

Senior Data Scientist

Lead Full-stack Data Scientist

Data Science Manager

Group Head of Data Science

Up to £200,000

London (Hybrid + occasional travel to Europe)

Company:

A leading international lottery and gaming group operating across Europe. They combine local expertise with global scale to deliver fun, responsible, and innovative gaming while generating meaningful impact for the communities we serve. They're now building a centralised data function to unify and scale data efforts across their group of businesses, using technology to drive transparency, efficiency, and growth.

Responsibilities:

  • Define and drive the vision for a centralised data science function across all brands and business units.
  • Develop and deliver the group-wide data strategy in alignment with executive leadership
  • Build out the team further over time, including hiring and capability development.
  • Foster collaboration and knowledge sharing among data teams in different countries and business units
  • Serve as the main point of contact for senior stakeholders across marketing, product, technology, and operations.
  • Ensure high standards in project delivery, governance, and impact measurement.
  • Drive adoption of modern machine learning methods and emerging technologies (including Generative AI).
  • Identify opportunities to improve efficiency, customer experience, and revenue through advanced analytics.

Requirements:

  • MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields.
  • Solid background in Data Science (though this is not a hands-on technical role)
  • Experience managing small, high-performing teams
  • A confident and strategic leader
  • Excellent communication and stakeholder management skills

How to Apply:

Please register your interest by sending your CV to Emily Burgess via the Apply link on this page

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.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.

Pre-Employment Checks for Machine Learning Jobs: DBS, References & Right-to-Work and more Explained

Pre-employment screening in machine learning reflects the discipline's unique position at the intersection of artificial intelligence research, algorithmic decision-making, and transformative business automation. Machine learning professionals often have privileged access to proprietary datasets, cutting-edge algorithms, and strategic AI systems that form the foundation of organizational competitive advantage and automated decision-making capabilities. The machine learning industry operates within complex regulatory frameworks spanning AI governance directives, algorithmic accountability requirements, and emerging ML ethics regulations. Machine learning specialists must demonstrate not only technical competence in model development and deployment but also deep understanding of algorithmic fairness, AI safety principles, and the societal implications of automated decision-making at scale. Modern machine learning roles frequently involve developing systems that impact hiring decisions, financial services, healthcare diagnostics, and autonomous operations across multiple regulatory jurisdictions and ethical frameworks simultaneously. The combination of algorithmic influence, predictive capabilities, and automated decision-making authority makes thorough candidate verification essential for maintaining compliance, fairness, and public trust in AI-powered systems.