Burberry Data Scientist

Burberry
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Customer Analytics & Personalization

Data Scientist – Personalization & Customer Insights

Data Scientist, Marketing Science — Drive Impact with ML

Data Scientist, Marketing Analytics & Impact

Burberry CRM Data Analyst (Maternity Cover)

CRM Data Analyst (Maternity Cover)

INTRODUCTION

At Burberry, we believe creativity opens spaces. Our purpose is to unlock the power of imagination to push boundaries and open new possibilities for our people, our customers and our communities. This is the core belief that has guided Burberry since it was founded in 1856 and is central to how we operate as a company today.


We aim to provide an environment for creative minds from different backgrounds to thrive, bringing a wide range of skills and experiences to everything we do. As a purposeful, values‑driven brand, we are committed to being a force for good in the world as well, creating the next generation of sustainable luxury for customers, driving industry change and championing our communities.


JOB PURPOSE

We are now recruiting for a Data Scientist to join the Customer Data Science team. The Customer Data Science team uses advanced modelling techniques to uncover our customers' behaviours, preferences and intent to purchase. We aim to deliver targeted and personalised experiences across all customer touchpoints in collaboration with business stakeholders.


As a Data Scientist, you will be accountable for building and deploying robust statistical and machine learning models, exploring a wide range of new data sources, and generating reliable and actionable insights and recommendations. You will work closely with other data scientists and engineers to develop innovative solutions to use cases across the business related to the Burberry customer. Some examples of current work include:



  • Propensity modelling: developing models to understand how likely a client or prospect is to purchase or interact with Burberry across multiple categories or time periods
  • Product recommendations & discovery algorithms: Serving customers with tailored product recommendations throughout many brand touchpoints such as the website and emails.
  • Client relationship intelligence: apply data‑driven models and strategies to enable client advisors to build long‑term engagement with their clients.

Are you a data scientist, or a recent graduate with an advanced degree in a quantitative field, and with a passion for using data, statistical modelling, machine learning and deep learning techniques to solve business problems and drive business value? If so, we'd love to hear from you.


RESPONSIBILITIES

  • Generating robust advanced analytics and developing new cutting‑edge machine learning models and data‑driven tools to support our ongoing business strategy and drive future business performance
  • Optimising and evolving the current models and analytics solutions that are in production, taking a test and learn approach and ensuring improvements are impactful and aligned to business objectives and strategy. Presenting the analytics solutions, models and insights to a range of business stakeholders and contributing to strategic decisions
  • Designing, evaluating and encouraging experimentation to demonstrate value across the business
  • Contributing new ideas towards improving our current solutions, processes and unsolved business problems
  • Working with the latest data science & AI technologies, and continuously learning and adapting in this fast‑evolving space
  • Being pro‑active and staying up to date with latest trends in analytics and technology

PERSONAL PROFILE

  • Advanced degree, MSc or PhD in a quantitative field (e.g., Data Science, Mathematics, Statistics, Econometrics, Computer Science, Physics, Engineering etc)
  • Some experience as a Data Scientist in a commercial environment and customer focused business or demonstrated experience with data science and statistical techniques.
  • Solid foundation in programming and experienced in Python.
  • Hands‑on experience in one or more of the following is preferred: time series, recommendation systems, customer journey modelling techniques, deep learning or large language models (LLMs)
  • Strong in problem‑solving, combining both a logical and innovative approach
  • Good, in‑depth, understanding of data science concepts and hands‑on practical use of mathematical, statistical, machine learning and deep learning techniques
  • Strong desire and proven ability to continuously learn new software, technologies and methodologies and keep up with latest data science trends & AI models
  • Collaborative approach to work, working in teams towards delivering a business objective.
  • Self‑starter, proactively identifying opportunities where analytics can add value and translating business requirements in analytical framework
  • Excellent communication skills with the ability to explain complex analytics to stakeholders

FOOTER

Burberry is an Equal Opportunities Employer and as such, treats all applications equally and recruits purely on the basis of skills and experience.


#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.

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.

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.

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.