At a Glance
- Tasks: Build predictive models and analyse data to enhance customer experiences.
- Company: Join a leading e-commerce platform focused on innovation and inclusivity.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Diverse workplace committed to equal opportunities for all.
- Why this job: Make a real impact in the world of data science and e-commerce.
- Qualifications: Master’s degree in Data Science or related field with 3+ years experience.
The predicted salary is between 50000 - 70000 £ per year.
Key Responsibilities
- Build predictive models and advanced analytics solutions to improve personalization, recommendation, and customer segmentation.
- Analyze large-scale datasets to identify behavioral trends, business opportunities, and product improvement areas.
- Partner with data engineers to ensure clean, reliable, and accessible data for experimentation and model deployment.
- Design and run A/B tests to measure the impact of algorithms and product features.
- Translate technical insights into clear, actionable recommendations for business and product stakeholders.
- Stay current with research and industry best practices in machine learning, NLP, and AI, applying them to real-world e-commerce challenges.
Qualifications
- Master’s degree or higher in Data Science, Statistics, Computer Science, or a related field.
- 3+ years of experience in data science, machine learning, or applied analytics.
- Proficiency in Python, R, or Scala, with strong skills in data analysis and statistical modeling.
- Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data visualization tools (e.g., Tableau, Power BI).
- Strong knowledge of A/B testing, experimentation design, and causal inference.
- Excellent communication skills, with the ability to present complex findings to non-technical stakeholders.
Preferred Qualifications
- Experience in global e-commerce, personalization, or recommendation systems.
- Familiarity with big data platforms (Spark, Hadoop) and cloud ML pipelines (AWS Sagemaker, GCP Vertex AI, or Azure ML).
- Background in NLP or computer vision applications for large-scale platforms.
At JD.com, we’re committed to building a diverse and inclusive workplace where everyone can thrive. We’re proud to be an equal‑opportunity employer and make all employment decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, veteran status, or any other protected characteristic.
Data Scientist employer: JINGDONG RETAIL (UK) LIMITED
At JD.com, we pride ourselves on fostering a dynamic and inclusive work environment that empowers our Data Scientists to innovate and excel. With access to cutting-edge technology and a commitment to professional development, employees can expect to grow their skills while contributing to impactful projects in the global e-commerce landscape. Our collaborative culture encourages creativity and ensures that every voice is heard, making it an ideal place for those seeking meaningful and rewarding employment.
Contact Details:
JINGDONG RETAIL (UK) LIMITED Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to fellow data scientists on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and analytics projects. We recommend using platforms like GitHub to share your code and visualisations – it’s a great way to impress potential employers.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past projects. We suggest practising common data science interview questions and even doing mock interviews with friends.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented data scientists like you. Plus, applying directly shows your enthusiasm and commitment to joining our team.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in data science and machine learning. Use keywords from the job description to show we’re on the same page about what you bring to the table.
Showcase Your Projects:Include specific examples of projects where you've built predictive models or conducted A/B tests. We love seeing how you've applied your skills in real-world scenarios, especially in e-commerce!
Keep It Clear and Concise:When writing your cover letter, be straightforward. We appreciate clear communication, so explain how your background aligns with our needs without going overboard on jargon.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at JINGDONG RETAIL (UK) LIMITED
✨Know Your Data Science Stuff
Make sure you brush up on your data science fundamentals, especially around predictive models and A/B testing. Be ready to discuss your past projects and how you've used Python or R to solve real-world problems.
✨Showcase Your Communication Skills
Since you'll need to translate complex findings for non-technical stakeholders, practice explaining your work in simple terms. Use examples from your experience where you successfully communicated insights to different audiences.
✨Familiarise Yourself with the Company’s Tech Stack
Research the tools and technologies the company uses, like TensorFlow or Tableau. If you have experience with big data platforms or cloud ML pipelines, be prepared to discuss how you've applied them in your previous roles.
✨Stay Current with Industry Trends
Keep up with the latest in machine learning and AI, especially as it relates to e-commerce. Bring up any recent research or trends that excite you and how they could apply to the company's challenges.
