Head of Growth Engineering

Fuse Energy, LLC
London
1 year ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Head of Data Science & ML Engineering

Head of Data Engineering

Up to £200,000 base + bonuses - Data Engineering Lead

Head of Machine Learning

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

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.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 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.