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

Apply Now

Head Of Data Science

Michael Page
Birmingham
2 weeks ago
Create job alert
Overview

The Head of Data Science will lead the Data Science department, helping drive data-driven strategies and insights to support public sector objectives. This role requires expertise in data science, leadership, and strategic implementation to deliver impactful results.


Client Details

My client is a regulatory body for solicitors and most law firms in England and Wales, aiming to protect the public by setting high professional standards and enforcing rules. We monitor standards, investigate misconduct, and can impose sanctions like fines or even closing firms. Its core goals are public protection, fostering trust in the legal profession, and supporting the rule of law and fair access to legal services.


Description

The successful Head of Data Science will be responsible for but not limited to:



  • Lead the development and implementation of data science strategies to support organisational goals.
  • Oversee the Analytics department, ensuring effective delivery of data-driven insights and solutions.
  • Collaborate with key stakeholders to identify opportunities for innovation and process improvement through data.
  • Manage and mentor a team of data professionals, fostering professional growth and skill development.
  • Develop predictive models and advanced analytics to inform decision-making processes.
  • Ensure compliance with data governance and ethical standards in all analytics activities.
  • Monitor industry trends and emerging technologies to maintain a competitive edge in data science.
  • Prepare and present reports on analytics outcomes to senior leadership and external stakeholders.

Profile

The successful Head of Data Science should have:



  • Strong expertise in data science methodologies, tools, and technologies.
  • Proven expertise in leading teams within the Analytics department or similar functions.
  • Ability to translate complex data findings into actionable insights for the public sector.
  • Strong understanding of data governance, ethics, and compliance frameworks.
  • Excellent communication and stakeholder management skills.
  • A relevant qualification in data science, computer science, or a related field.

Technical knowledge

Expert use of standard statistical tools e.g. R/Python and relevant associated libraries


Deep expertise in building and maintaining AI and machine learning models, including use of deep learning, natural language processing, and LLMs.


Job Offer

With offices in Birmingham and London, this role is suitable for anyone able to either location 2 days a week.



  • Salary Circa 70,000
  • Comprehensive benefits package including
  • 25 days holiday rising to 27
  • Combined matched pension up to 19%
  • Hybrid working.
  • A flexible benefits scheme that you can tailor depending on your needs.
  • Opportunities to shape the future of data science within the organisation.

Help us to make a significant impact in the legal regulatory industry as our Head of Data Science. Apply today to take the next step in your career!


#J-18808-Ljbffr

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Head of Data Science

Head Of Data Science

Head Of Data Science

Head of Data Science

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.