Data Scientist – Personalization & E‑commerce AI in London

Data Scientist – Personalization & E‑commerce AI in London

London Full-Time 50000 - 70000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Create data-driven insights and machine learning models for e-commerce personalisation.
  • Company: Join JD.com, a leader in international e-commerce innovation.
  • Benefits: Enjoy competitive pay, flexible work options, and growth opportunities.
  • Other info: Fast-paced environment with exciting challenges and career advancement.
  • Why this job: Make a real impact on customer experiences and product features.
  • Qualifications: Experience in data science and collaboration with cross-functional teams.

The predicted salary is between 50000 - 70000 £ per year.

JD.com is seeking a Data Scientist to develop data-driven insights and machine learning models powering personalization, recommendations, and analytics across its international e-commerce operations. The role collaborates with data engineers, product managers, and business teams to transform raw data into business intelligence and improve decision-making, customer experience, and product features in a fast-paced environment.

Data Scientist – Personalization & E‑commerce AI in London employer: JINGDONG RETAIL (UK) LIMITED

JD.com is an exceptional employer for Software Engineers, offering a dynamic and inclusive work environment that prioritises employee growth and development. With competitive salaries and access to global projects, team members can thrive while collaborating on innovative solutions in the fast-paced e-commerce sector across Europe. The company's commitment to a people-first culture ensures that every employee feels valued and empowered to contribute their best work.

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Contact Details:

JINGDONG RETAIL (UK) LIMITED Recruitment Team

We think you need these skills to ace Data Scientist – Personalization & E‑commerce AI in London

Machine Learning
Data Analysis
Business Intelligence
Collaboration
E-commerce Analytics
Personalisation Techniques
Recommendation Systems