Sanderson | Head of Product / Product Lead

Sanderson
London
1 year ago
Applications closed

Related Jobs

View all jobs

BI and Data Engineer

Data Engineer - DV Cleared

Data Engineer - DV Cleared...

Data Engineer – Hybrid, Real-Time Data Pipelines

BI and Data Engineer

Data Engineer - DV Cleared

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.