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

Apply Now

Sanderson | Head of Product / Product Lead

Sanderson
London
9 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Operational / Data Engineer

Machine Learning Engineer

Data Engineer (SC)

Machine Learning Engineer

Operational / Data Engineer

Head of Product / Product Lead / Senior Product Manager / AI Product Manager / Role purpose:This role will ensure we achieve a set of agreed outcomes across a substantial program of work, by creating and then delivering a roadmap with a continuous focus on quality, pace and the accurate measurement of impact. The role will focus will on optimising our business and service operations using cutting-edge predictive and prescriptive AI models, Data Science and Machine Learning to improve operational efficiency, reduce costs, and enhance customer satisfaction.The role:Define the product vision, goals, and roadmap, ensuring alignment with organisational objectives in line with the AI Strategy. Gather requirements from stakeholders, including operational teams and leadership, and translate them into actionable deliverables.Prioritise features and tasks based on business value, technical feasibility, and timelines.Collaborate with the team of Data Scientists and Engineers to develop innovative solutions for deployment optimisation.Partner with internal teams to ensure smooth integration of project into existing systems and business processes.Monitor project progress, manage risks, and address roadblocks to ensure timely delivery.Define success metrics and KPIs for AI initiatives and monitor their performance post-launch.Drive continuous improvement by incorporating feedback and analysing results.Communicate project updates, insights, and progress to stakeholders.Experience / Skills:Proven experience as a Product Manager / Leader in a technical or data-driven environment.Strong understanding of AI, Data Science, and Machine Learning applications.Exceptional communication, stakeholder management, and organisational skills. Able to convey ideas and technical content to different stakeholders, from engineers to senior executives.Experience with Agile methodologies and managing cross-functional teams.Experience of owning a complex data science/ Gen AI problem from ideas and discovery through to prioritisation, definition, delivery and post launch evaluation. Demonstrating sound decision making at each stageData Proficiency and Collaboration: Skilled in analysing raw data and using SQL and other data tools to visualise insights; effectively translates complex data needs into clear requirements for data science/Gen AI teams and actionable recommendations for stakeholders.Sufficient understanding of software development, data science and GenAI processes and design principles to be able to communicate and collaborate effectively with technical team; and to assess the implications of technical decisions on the product strategy and user experience.Track record of defining and delivering great analytical outcomes leading to commercial outcomes – and adept at balancing the two.

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.