Lead Data Scientist

Department:  Digital
City:  London
Location:  GB

Founded in 1856 by Thomas Burberry, Burberry is a global luxury brand with a distinctly British attitude. We are a global business with an extensive network of both owned and franchised stores across EMEIA, Asia Pacific and Americas. We are digital pioneers, and innovative technology underpins every aspect of our business, from product design to distribution and marketing. We believe that modern luxury means being socially and environmentally responsible; this mindset is core to our business and key to our long-term success.


Burberry is making a major investment in expanding our data and analytics capability. Over the last few years we have created a global omni-channel single customer view, built an outstanding and modern analytics platform and developed a small but highly effective analytics and data science team. We’re now ready to build on these foundations by growing both teams, to drive and embed data-inspired decision making across Burberry. 

We are recruiting a Lead Data Scientist to join our fast-expanding data science team and lead a team of very high-calibre and ambitious data scientists.  

As a Lead Data Scientist, you will be accountable for data science delivery in one of our core data strategy areas:  

  1. Personalization, Precision Marketing & Customer Journeys: Working across all customer touchpoints to deliver targeted or personalised experiences to our customers 
  2. Supply & Demand Optimisation: Working to enhance both our supply chain and our merchandising, allocation and planning processes. 

You will be responsible for refining the data science roadmap and driving your team to deliver innovative and robust analytics, models and data products under an agile framework. 


Do you have a track record in leading ambitious and innovative Data teams? If so, we’d love to hear from you.


Opportunity Scanning: You’ll be responsible to identify and qualify business ideas, in consultation with your stakeholders, and translate them to data science opportunities and deliverables. You’ll encourage a data-first approach, bridging the gap between data science and decision making, and ensuring data science is well embedded in the business 

Strategy & Planning: You’ll define the data science agenda and 6-12-month roadmap of data science projects in your area, in alignment with business and stakeholder objectives 

People Management: You’ll recruit, manage and develop a team of 4-6 data scientists 

Delivery Management: You’ll lead value-led prioritisation of your team’s work, ensuring excellence in day-to-day operational and project delivery. You’ll act as a project lead and lead cross-functional teams to deliver under an agile project-based framework 

Insight Delivery: You’ll directly lead on the delivery of important/complex projects - leading the analytical effort, synthesising key insights, creating and sharing the narrative and recommending our forward strategy   

Communication: You’ll ensure that the work and contribution of Data Science is shared, recognised and well understood 

Toolset & Data Strategy: You’ll contribute to Burberry’s big data and technology roadmap with the objective to evolve and optimise your teams’ work and deliverables 

Horizon Scanning: You’ll invest in staying up to date on the latest data science and technology trends and working out where they can be leveraged at Burberry

  • Advanced degree, MSc or PhD in a quantitative field (eg Mathematics, Statistics, Computer Science, Physics, Engineering etc) 
  • At least 5 years’ experience as a Data Scientist / Data Science Lead in a commercial environment 
  • Experience in the retail sector or other customer focused and digital business is preferred 
  • Experience in merchandising, planning, supply chain or personalisation is particularly desirable 
  • Proven personal portfolio of projects, both as a direct contributor and as a lead, where analytics and data science were used to drive business value and decisions 
  • Strong in problem-solving, combining both a logical and innovative approach  
  • Good, in-depth, understanding and extensive practical use of mathematical, statistical, machine learning and deep learning techniques 

Job Segment: Database, Scientific, Merchandising, Engineer, Supply, Technology, Engineering, Retail, Operations