CRO Manager

In Digital
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Technical SEO Executive

Conversion Rate Optimisation (CRO) Manager


We’re In Digital, a thriving digital growth agency based in Covent Garden and ranked as one of the best places to work in the UK – and we’re currently seeking a Conversion Rate Optimisation (CRO) Manager who will have the opportunity to contribute to our diverse range of projects.


At In Digital, you will have the chance to practice and hone your skills as an all-round CRO expert, with the opportunity to contribute and lead across the full cycle of optimisation, from strategy to analytics via planning, UX, design, build, optimisation, technology and more.


Get a better feel for who we are on our website:https://indigital.marketing/


Who we’re looking for – at a glance:

  • 3 + years of experience in CRO (in-house or agency)
  • Confidence using big data sets when using web analytics tools, performing complex analysis and collecting & driving improvement ideas to drive revenue from customer insights
  • Strong experience using website analytics platforms: GA4 (must have), Adobe, Amplitude, Mixpanel
  • Ideal hands-on experience with the following testing tools: Optimizely, VWO, Piano, Zephr
  • Experience working with analytics teams to set up test-specific tracking for test reporting
  • Experience creating test plans, using split testing software, and analysing test results
  • Analytical mindset - comfortable handling quantitative data and understanding logic and statistics behind A/B testing
  • An interest and an understanding of other digital marketing areas and how they interlock with CRO
  • Nice to have but not essential: knowledge of HTML/CSS/JS


What we offer:

  • Extra fast learning & development across a broad range of skillsets
  • Training & development through courses, mentoring and hands-on execution
  • Ample opportunity for career development and salary progression
  • Bi-annual bonuses for all employees
  • A brilliant workplace set over three floors in the heart of Covent Garden with our own cocktail bar & roof terrace, great coffee, snacks and discounts on local shops
  • One week per year of “work from abroad” allowance in addition to holiday allowances
  • Subsidised gym & sports memberships
  • Private pension plan
  • & a range of other benefits, including regular team lunches, events, charity days, sporting activities


Typical responsibilities for a CRO Manager:

  • Testing strategy and programme management: set the agenda for our clients, create and manage a testing plan to help deliver long term results and maturation of your clients’ testing capabilities
  • Analytical approach to value-driving experimentation: Aligning to business goals, aim to drive commercial impact through revenue, conversions and minimal cost impact.
  • Excellent client management skills, capable of bringing together client teams as relevant to the testing roadmap, and building strong relationships across teams
  • Identifying problems and opportunities with the conversion rate of client websites through quantitative and qualitative research
  • Create data-backed test hypotheses to address issues identified in quant and qual research and use your conversion expertise to design effective solutions
  • Work with data and dev teams to manage end-to-end funnel tracking
  • Analyse and evaluate experiments: analysis of conversion funnel, user behaviour and engagement to devise iterations on concepts and drive the testing strategy
  • Prioritise insights and testing strategy which helps our clients achieve their goals and delivers ROI
  • Results-oriented, taking accountability for success for your clients
  • Collaboration: working with the rest of the consulting team and across clients to provide feedback on strategy and contribute to brainstorms for client tests

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.