National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Head of Growth Engineering

Fuse Energy, LLC
London
5 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering

Head of Data Engineering & Analytics

Head of Data Science

IT Manager (Manufacturing)

Data Scientist

Senior Data Engineer

About Fuse Energy

Fuse Energy is a forward-thinking company on a mission to redefine how energy solutions reach and engage customers. As we scale our operations, we seek a technically adept Head of Growth Engineering to lead our efforts in building an advanced, performance-focused growth engine.

Role Overview

The Head of Growth Engineering will be a pivotal figure in shaping and executing Fuse Energy’s growth strategies. This role blends the worlds of engineering, marketing, and data science to build an agile, high-performance engine that drives customer acquisition and maximises revenue generation through sophisticated, tech-driven approaches. You will manage and optimise systems that support targeted, data-backed marketing initiatives, enabling the company to make real-time, data-driven decisions and scale effectively.

This position requires a deep understanding of both marketing principles and technical engineering concepts. You will collaborate closely with the growth and product teams to ensure seamless integration of technology with business goals. The ideal candidate will have a strong engineering background and the ability to work across technical and non-technical domains to drive impactful outcomes.

Key Responsibilities

  • Develop and execute a growth engineering strategy that aligns with Fuse Energy’s business objectives and revenue targets. Balance long-term innovation with short-term, data-backed optimisation for immediate growth.
  • Build and maintain technical infrastructure for performance marketing. Oversee the ongoing development and enhancement of algorithms, automation tools, and AI-driven processes that streamline and improve the effectiveness of our marketing efforts.
  • Champion a data-centric approach to decision making, ensuring that all growth activities are backed by robust data and analytics.
  • Stay ahead of industry trends, particularly in AI, machine learning, and digital marketing technologies, to continuously innovate and keep Fuse Energy at the forefront of the energy sector.

Minimum Qualifications

  • Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.
  • Professional experience in engineering, with a focus on the creative industry (marketing experience not needed).
  • Ability to work with complex technical concepts and apply them to a creative field.
  • Proven experience leading high-performing teams in a fast-paced environment. Strong communication skills and the ability to motivate, influence, and guide technical and non-technical teams alike.
  • A natural problem solver with the ability to think critically and strategically to tackle complex challenges.

Benefits

  • Competitive salary and a stock options sign-on bonus.
  • Biannual bonus scheme.
  • Fully expensed tech to match your needs!
  • 30 days paid annual leave per year (including bank holidays).
  • Deliveroo breakfast and dinner for office-based employees.

#J-18808-Ljbffr

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.