Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop data science models to enhance e-commerce growth and customer experience.
- Company: Leading e-commerce company focused on innovation and customer satisfaction.
- Benefits: Competitive salary, company equity, 25 days annual leave, and additional benefits.
- Why this job: Join a dynamic team and make a real impact in the e-commerce industry.
- Qualifications: Degree in Data Science, 2+ years experience, and skills in Python and SQL.
- Other info: Exciting opportunities for career advancement in a fast-paced environment.
The predicted salary is between 65000 - 70000 £ per year.
A leading e-commerce company seeks a Data Scientist to improve growth and customer experience. You will develop data science models for e-commerce use cases, analyse experiments, and communicate insights.
Required qualifications include:
- A degree in Data Science
- Over 2 years of relevant experience
- Expertise in Python and SQL
The role offers a competitive salary (£65,000-£70,000), company equity, and benefits including 25 days annual leave plus bank holidays.
Data Scientist – Growth & Equity for E-commerce employer: OnBuy Limited
Contact Detail:
OnBuy Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – Growth & Equity for E-commerce
✨Tip Number 1
Network like a pro! Reach out to people in the e-commerce space, especially those working as Data Scientists. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those relevant to e-commerce. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your Python and SQL skills. Be ready to discuss your past experiences and how they relate to improving growth and customer experience in e-commerce.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Data Scientist – Growth & Equity for E-commerce
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python and SQL in your application. We want to see how you've used these skills in real-world scenarios, especially in e-commerce contexts.
Be Data-Driven: Since this role is all about data science, include specific examples of models you've developed or experiments you've analysed. We love numbers, so don't shy away from showcasing your impact with quantifiable results!
Tailor Your Application: Take a moment to customise your CV and cover letter for this role. We appreciate when candidates connect their experiences directly to the job description, showing us why you're the perfect fit for our team.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at OnBuy Limited
✨Know Your Data Science Models
Make sure you brush up on the data science models relevant to e-commerce. Be ready to discuss how you've applied these models in past projects, especially those that improved growth or customer experience.
✨Show Off Your Python and SQL Skills
Prepare to demonstrate your expertise in Python and SQL during the interview. You might be asked to solve a problem on the spot, so practice coding challenges and be ready to explain your thought process clearly.
✨Understand the Company’s Growth Strategy
Research the company’s current growth strategies and recent initiatives. This will help you tailor your answers to show how your skills can directly contribute to their goals, making you a more attractive candidate.
✨Communicate Insights Effectively
Since the role involves communicating insights, practice explaining complex data findings in simple terms. Use examples from your previous work where you successfully conveyed insights to non-technical stakeholders.
