Lead Data Scientist - Pricing

Just Eat Takeaway.com
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Position:  Lead Data Scientist - PricingFull Time

Ready for a challenge? 

About this role

The Revenue Management & Pricing team owns the consumer pricing agenda globally at JET, ensuring development of business-critical ML capabilities and features, and ultimately delivering on revenue and growth targets to our business. We design prices to align both customers and partners interests while maximising on the value created by our marketplace.

Reporting to our Global Head of Data Science (Pricing & Marketing), we are seeking a highly skilled Lead Data Scientist to join our team to deliver all the aspects of our consumer pricing strategy, including experimentation, data analysis and pricing optimization.

These are some of the key components to the position: 

  • Commercial excellence: to develop and implement market leading pricing strategies that optimise growth and margin across the countries where we operate. Recommend commercial strategies, tactics and guidelines, collaborate with local markets senior stakeholders to agree on optimal approach.

  • Pricing modelling, experimentation and execution: capture data, develop ML models and tools and apply statistical modelling to determine the impact of our pricing actions. Run experiments in a commercial environment which require lateral thinking and deep statistical understanding. 

  • MLE partnership: partner closely with Machine Learning Engineers, who own the deployment and scaling processes, to transition robust pricing model designs and thoroughly documented algorithmic logic for production implementation.

  • Insights generation and analysis: guide commercial stakeholders in deep-dive analysis of large-scale customer data to translate findings into actionable pricing recommendations and tools. Conduct ad-hoc analyses requested by senior management and key stakeholders to support strategic decision-making processes.

  • Leadership & innovation: take full ownership of complex data science projects from the initial what if question to final delivery. Proactively identify opportunities within consumer pricing and our logistics operations where machine learning and pricing optimization can drive growth, cost-savings and logistics network efficiency.

What will you bring to the team?

  • Strong knowledge of underlying mathematical foundations of statistics, exploratory analysis, economics in the ecommerce domain and analytics (prescriptive and predictive), testing and modelling. Experience in Bayesian modelling is not essential but highly desirable.

  • Experience developing, testing, deploying, and maintaining machine learning models in a corporate environment.

  • Strong knowledge of experiment design & setup with the ability to define business goals,  identify variables (price points, pricing structures, segmentation, etc.), choose experiment methodology and plan implementation.

  • Exceptional communication and stakeholder management skills, with the ability to influence technical peers and non-technical business leaders.

  • An analytical problem solver who values simplicity, brings clarity to ambiguous questions, and communicates complex insights effectively to any audience.

  • Exceptional attention to detail and proactive mindset with a continuous improvement approach, constantly seeking ways to enhance our pricing capabilities and tools. 

  • Bachelor or Master's degree in Data Science, Computer Science, Statistics or a related quantitative field.

At JET, this is on the menu: 

Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. 

Fun, fast-paced and supportive, the JET culture is about movement, growth and about celebrating every aspect of our JETers. Thanks to them we stay one step ahead of the competition.

Inclusion, Diversity & Belonging 

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.