Chief Technology Officer

Gloo
London
2 months ago
Applications closed

Related Jobs

View all jobs

Chief Technology Officer / CTO

Chief Technology Office

Defence Digital - Finance Manager

IT Security Engineer, Senior, London, Bank

Security Engineer, Senior, London, Bank 75k

Chief Architect - Skynet Ground Systems

Chief Technology Officer (CTO) - £250k


Location:Central London


Are you ready to lead the technological vision of a company revolutionizing customer intelligence? We are seeking a visionaryChief Technology Officer (CTO)to spearhead the development and execution of innovative technology strategies, empowering businesses to deliver personalized, data-driven customer experiences.


About Us:

We are a cutting-edge AI company helping enterprises unlock the full potential of their customer data. Our platform combines advanced data science, machine learning, and behavioral insights to provide actionable intelligence for some of the world's leading brands. Join us as we scale our platform and transform the way businesses connect with their customers.


The Role:

As CTO, you will shape and drive the company’s technology strategy, ensuring that our AI-powered platform continues to be an industry leader. Reporting directly to the CEO, you’ll collaborate with cross-functional teams to deliver scalable, secure, and innovative solutions that meet and exceed client expectations.


Key Responsibilities:

  • Develop and execute a forward-looking technology roadmap aligned with business goals.
  • Lead, inspire, and grow a talented engineering and data science team.
  • Ensure the scalability, security, and reliability of our technology infrastructure.
  • Drive the adoption of cutting-edge technologies to enhance product innovation.
  • Collaborate with stakeholders to translate market opportunities into technical solutions.
  • Build strong partnerships with external technology providers and stakeholders.
  • Maintain a focus on ethical AI and data governance practices.


What We’re Looking For:

  • Proven experience in a senior technology leadership role (e.g., CTO, VP Engineering, or similar).
  • Expertise in AI, machine learning, and data science, with a focus on scalable platforms.
  • Strong understanding of cloud infrastructure and SaaS architecture.
  • A track record of leading engineering teams through periods of growth and innovation.
  • Exceptional strategic thinking with the ability to communicate complex ideas effectively.
  • Passion for creating customer-centric solutions that deliver measurable impact.


What We Offer:

  • Competitive salary and meaningful equity in a rapidly growing company.
  • Flexible, hybrid working environment.
  • The opportunity to shape the future of customer intelligence technology.
  • Collaborative and supportive company culture.

Join Us:

If you’re a visionary technologist with a passion for innovation and a drive to make a real impact, we’d love to hear from you. Help us redefine how businesses understand and engage their customers.

Apply Now!

To express your interest or request more information, please email [email address] with your CV and a brief statement of your experience and vision.


"Customer Intelligence" OR "Behavioural Insights" OR "Customer Analytics" OR "Personalized Marketing" OR "Customer AI" OR "Privacy-First AI" OR "Customer Prediction Models" OR "Privacy-Safe Insights" OR "First-Party Data" OR "Second-Party Data" OR "Data Enrichment" OR "Data Marketplace" OR "Insight Store" OR "Market Explorer" OR "AI-Powered Dynamic Reports" OR "CRM Enrichments" OR "Personal Behavioural Models" OR "Omnichannel Marketing" OR "Churn Prediction" OR "Retention Strategies" OR "Predictive Analytics" OR "Customer Segmentation"

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.

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.