Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Scientist - MLOps

Global
City of London
1 week ago
Create job alert

Accepting applications until: 28 November 2025


Job Description
Job title: Lead Data Scientist - MLOps
We are Global

At Global, we think big, work hard, and never stand still. We’re the proud home of the best media and entertainment, driven by our talented and passionate people. Our mission? To make everyone’s day brighter - our Globallers, our audiences, our partners, and our communities. Whether we’re in the studio, building world‑class technology, or securing record Outdoor advertising partnerships, we make sure we’re doing it as a team.


Your new role

The Data team in Global is recruiting for a Lead Data Scientist with a strong foundation in MLOps to join an experienced team of data scientists, analysts and engineers. The role will be responsible for modelling, analysing and communicating to business stakeholders key metrics to drive commercial success as well as productionising new and existing products in a scalable, robust and efficient manner. The role will involve a large degree of collaborating and working cross‑functionally with teams in our digital ad exchange platform (DAX), as well as exposure to our commercial, outdoor and insights teams.


We are looking for a person with a data science and engineering skillset, and an analytical mindset that takes keen interest in data, the insight they can provide and the opportunities they unlock. The role gives the opportunity to work in a well‑established data team in a fun, informal environment, working together with talented, likeminded people that have “a thing” for data.


The role is based in central London (primarily in Holborn, with occasional travel to our site at Leicester Square).


Key Responsibilities

  • Model Development & Analytics (50%)


  • Research and implement supervised and unsupervised learning techniques to improve Global’s internal business processes and propose new products to market.
  • Actively work on prototyping and evaluating new and current machine learning algorithms sitting behind the scenes with Global.
  • Present insights and recommendations clearly to both technical and non-technical audiences.


  • MLOps & Productionisation (50%)


  • Build and maintain scalable pipelines for model training, validation, deployment and monitoring.
  • Refactor and optimise legacy models and processes for production use (performance, reliability, cost‑efficiency).
  • Automate model lifecycle management (retraining, versioning, monitoring etc).
  • Work closely with the data and analytics engineering teams to deploy models into production environments.
  • Supporting others in the data science and wider data team on MLOps best practices, and ensuring consistency of deployment across projects.

General responsibilities

  • Leading cross functional teams to deliver end to end data science projects.
  • Mentoring junior members of the data team, both technically and professionally.
  • Stakeholder management – understanding how the output from the team can be used and have a positive effect in downstream business processes.
  • Explore and develop new data science products connected to our DAX platform to provide actionable insight and drive commercial value, touching on areas such as targeting, measurement, reach, and beyond.
  • A flexible attitude towards learning new skillsets to adapt and meet the Data teams and wider business needs, including learning about and deeply understanding the advertising problem space and data opportunities.
  • Contribution to the team spirit. We are a supportive and a high performing group and looking for someone who will bring curiosity and active involvement to the way that we work together with each other and the rest of the business.

What You’ll Love About This Role

Think Big: Innovate with ML and AI solutions that can transform our business and drive growth.


Own It: Lead products end-to-end – from idea to launch – and see the direct impact of what you build.Keep it Simple: Turn complex data science concepts into simple, effective and automated tools and processes.


Better Together: Collaborate with diverse teams across Global, where every perspective is valued.


What Success Looks Like

In your first few months, you’ll have:



  • An understanding of our business operating model, especially in DAX but also across our other business streams (Audio and Outdoor) and knowledge of our datasets, applications and the data workflows that we work with.
  • Exposure to our existing suite of data science products, solutions and tools across targeting, measurement and consumer analytics.
  • Deployed at least one data science product within DAX (new or legacy) into a scalable production environment.

What You’ll Need

  • Demonstrable understanding and experience in predictive, prescriptive and generative ML/AI methodologies.
  • A strong degree in a numerate subject (e.g., maths, data science, computer engineering).
  • Expertise in Python and SQL – generally someone who gets excited by code.
  • Experience with MLOps platforms (e.g., MLflow, Snowflake intelligence) and CI/CD pipelines.
  • Extensive experience deploying, monitoring and maintaining ML and AI models in production.Strong understanding of model performance monitoring, logging, and alerting frameworks.
  • Knowledge of software engineering best practices (version control, testing, code reviews).
  • An exceptional eye for detail, ability to work under pressure and deliver time‑sensitive work to a high standard.
  • A professional manner with the ability to closely interact with senior management.
  • The ability to lead and mentor others, in your immediate team and beyond.
  • The ability to explain analysis to non‑numerate people and empathise with their perspective.
  • The ability to build and maintain relationships with a diverse audience.
  • A can‑do attitude, positivity around data challenges and generally a natural inclination for extrapolating business problems and providing solutions utilising data.

Creating a place we all belong at Global

We are dedicated to creating a place where different voices are represented, amplified and celebrated. Weknow that we can’tserve our diverse audiences without first celebrating it in our people, which is why we’re passionate about creating an inclusive culture where every Globaller can belong. So, no matter who you are or where you are from, you can find your place at Global.


As a business, we believe in the importance of a healthy work‑life balance and the value of a flexible and agile workforce. Therefore, we operate a Smart Working approach. If you need us to make any reasonable adjustments during your recruitment process, drop us an email at , we’ll be happy to help.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist – Project and Capability

Lead Data Scientist

Lead Data Scientist

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.