Operations Director London, England, United Kingdom

Tbwa Chiat/Day Inc
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Senior Data Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Data Scientist / Software Engineer

Lead Data Engineer (Azure)

Established in 2004,OLIVERis the world’s first and only specialist in designing, building, and running bespoke in-house agencies and marketing ecosystems for brands. We partner with over 300 clients in 40+ countries and counting. Our unique model drives creativity and efficiency, allowing us to deliver tailored solutions that resonate deeply with audiences.

As a part ofThe Brandtech Group, we're at the forefront of leveraging cutting-edge AI technology to revolutionise how we create and deliver work. OurAI solutionsenhance efficiency, spark creativity, and drive insightful decision-making, empowering our teams to produce innovative and impactful results.

Role: Operations Director

Location:London, England, United Kingdom

Role Mission:As the Operations Director, you will be the backbone of our client team in London, driving operational excellence and ensuring outstanding work and commercial success. You will oversee key operational metrics, lead process improvements, optimize our project management platform (OMG), and provide actionable insights to elevate business performance.

This Role is Right for You If:

  • You thrive as a strategic problem-solver who partners effectively with clients and team leads.
  • Building robust relationships with stakeholders and challenging the status quo excites you.
  • Leading a team motivates you, and you're passionate about fostering an environment of innovation and operational excellence.
  • You take ownership of financial processes, optimize profitability, and engage in strategic decision-making.

What Skills Will Help You Be Successful:

  • Over 10 years of agency operations experience with a strong commercial focus.
  • A successful track record in improving processes and managing complex metrics.
  • An analytical mindset that turns data into decisive actions and solutions.
  • Exceptional stakeholder management and problem-solving capabilities.
  • Self-starter attitude with a proactive approach to challenges.
  • Comfort with technology and the enthusiasm to champion AI and new systems.

Our values shape everything we do:

BeImaginativeto push the boundaries of what’s possible.

Bealways learning and listeningto understand.

Beactively pro-inclusive and anti-racistacross our community, clients, and creations.

OLIVER, a part of the Brandtech Group, is an equal opportunity employer committed to creating an inclusive working environment where all employees are encouraged to reach their full potential, and individual differences are valued and respected. All applicants shall be considered for employment without regard to race, ethnicity, religion, gender, sexual orientation, gender identity, age, neurodivergence, disability status, or any other characteristic protected by local laws.

OLIVER has set ambitious environmental goals around sustainability, with science-based emissions reduction targets. Collectively, we work towards our mission, embedding sustainability into every department and through every stage of the project lifecycle.

#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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.