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

Apply Now

Product Manager – Digitalization

Ellesmere Port Town
5 months ago
Applications closed

Related Jobs

View all jobs

Claims Data Scientist

Data Scientist - Placement Year

Product Manager, AdTech and Machine Learning London, UK • Advertising Technology +1 more • Prod[...]

Product Manager, AdTech and Machine Learning

Senior Product Manager - Machine Learning and AI

Principal Product Manager, Data Science & Machine Learning

Product Manager – Smart Diagnostics & Digitalization

Location: Remote
Industry: Feed & Biofuel / Renewable Energy / Industrial Automation
Type: Full-Time | Permanent

About Us

We are working with a global leader in the design and construction of advanced feed and biomass production plants. Their mission is to deliver high-performance industrial solutions that maximize sustainability, efficiency, and uptime. As they expand their digital capabilities, we are looking for a Product Manager with a strong mechanical background and expertise in smart diagnostics and digital product development.

Role Overview

As Product Manager for Smart Diagnostics, you will lead the development of innovative digital tools to monitor and diagnose mechanical assets across a global customer base. This includes leveraging machine learning, sensor data, and domain expertise to reduce downtime and improve operational efficiency.

You’ll play a key role in conceptualizing, designing, and delivering digital products from scratch, bridging the gap between mechanical engineering and next-gen digital solutions.

Key Responsibilities

  • Design and develop digital diagnostic products for mechanical assets (e.g., pellet mills, conveyors, grinders).

  • Define and own the product roadmap for smart maintenance and condition monitoring solutions.

  • Utilize machine data, vibration analysis, and performance metrics to predict failure modes and optimize service schedules.

  • Apply machine learning models to real-world machine behavior in feed and biomass plants.

  • Collaborate with software engineers, data scientists, service engineers, and plant designers.

  • Engage with customers and stakeholders to understand their pain points and tailor solutions.

  • Lead end-to-end product lifecycle from idea to commercial launch.

  • Ensure full alignment with engineering, digital development, and commercial teams.

  • Contribute to building an intelligent service platform for the industry of tomorrow.

    What We’re Looking For

  • Mechanical Engineering degree or similar technical background.

  • Proven experience in diagnostic systems, predictive maintenance, or condition monitoring.

  • Strong understanding of mechanical asset behavior in industrial environments.

  • Experience applying machine learning models or working alongside data science teams.

  • Ability to create digital products from the ground up in a structured and user-centric way.

  • Excellent communication and cross-functional collaboration skills.

  • Experience in the Feed & Biofuel or biomass processing industry is highly desirable.

  • Fluent in English; additional languages are a plus.

    What We Offer

  • A key role in shaping the future of digital maintenance in renewable industries.

  • Opportunity to work with cutting-edge technologies and meaningful industrial applications.

  • A collaborative, international environment with significant autonomy.

  • Competitive salary and benefits package.

  • Travel opportunities and career progression within a global leader

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.