At a Glance
- Tasks: Address complex customer understanding issues and implement best practices in modelling and experimentation.
- Company: Sainsbury's is a leading retailer focused on enhancing customer data science initiatives.
- Benefits: Join a dynamic team and elevate your expertise in machine learning and personalisation.
- Other info: Position located within the Customer Data Science team.
- Why this job: This role offers the chance to coach team members and influence decision-making processes.
- Qualifications: Expertise in machine learning and experience in technical standards are required.
The predicted salary is between 70000 - 90000 £ per year.
Sainsbury's is seeking a Senior Data Scientist – Technical Specialist to join its Customer Data Science team. In this role, you will address complex issues in customer understanding and personalisation, implementing best practices in modelling and experimentation.
Your expertise in machine learning will be essential in enhancing decision-making processes and coaching team members on technical standards. Join a leading retailer and elevate its data science initiatives.
Senior Data Scientist, Personalisation & ML Ops in London employer: Experimentation Jobs
Sainsbury's, a prominent UK retailer, offers a collaborative environment with opportunities for professional growth. The Customer Data Science team focuses on innovative solutions in personalisation and customer understanding. Employees benefit from a culture of continuous learning and development.
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We think this is how you could land Senior Data Scientist, Personalisation & ML Ops in London
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We think you need these skills to ace Senior Data Scientist, Personalisation & ML Ops in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
