Data Scientist III - Experimentation Science (Statistical Methodologies)

Expedia Group
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Scientist III, Analytics (B2B Supply Optimisation)

Data Scientist III, Analytics (B2B Supply Optimisation)

Data Scientist

Data Scientist

Consumer Lending Data Scientist

Data Scientist - Imaging - Remote - Outside IR35

Job Description

Introduction to team:

As a Data Scientist III on our Experimentation Science team, you will play a critical role in shaping the statistical methodologies that underpin Expedia Group’s experimentation platform. Your work will ensure that every feature change on our website—from button colors to booking flows—is rigorously tested to maximize business impact.


This role blends research and practical application, requiring you to design, evaluate, and improve hypothesis testing frameworks and experimentation techniques. You will collaborate closely with platform engineers to implement scalable solutions, provide expert guidance to stakeholders, and drive innovation through cutting-edge statistical research.


This position offers a unique opportunity to apply deep statistical expertise in a dynamic environment where your contributions directly influence product decisions and customer experiences worldwide.


In this role, you will:

  • Lead the development and validation of advanced statistical methodologies to support Expedia Group’s experimentation platform, ensuring robust and reliable A/B testing across the website’s features and user experiences
  • Collaborate closely with the experimentation platform team to design, implement, and scale new testing frameworks and tools that enh...

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.

Careers Jobs

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.