Head of Growth Engineering

Griffin Fire
London
3 months ago
Applications closed

Related Jobs

View all jobs

Head of Commercial Insights

Head of Research & Development (EMEA)

Head of Data & AI

Head of Data & AI

Programmatic Solutions Consultant , Amazon Ads Global Sales

Senior Data Analyst

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

  1. 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.
  2. 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.
  3. Champion a data-centric approach to decision making, ensuring that all growth activities are backed by robust data and analytics.
  4. 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 Requirements

  1. Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.
  2. Professional experience in engineering, with a focus on the creative industry (marketing experience not needed).
  3. Ability to work with complex technical concepts and apply them to a creative field.
  4. 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.
  5. A natural problem solver with the ability to think critically and strategically to tackle complex challenges.

Benefits

  1. Competitive salary and a stock options sign-on bonus.
  2. Biannual bonus scheme.
  3. Fully expensed tech to match your needs!
  4. 30 days paid annual leave per year (including bank holidays).
  5. Deliveroo breakfast and dinner for office-based employees.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.