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- Tasks: Design and deliver analytical Python solutions using machine learning techniques.
- Company: Leading data science company in Greater London with a focus on diversity.
- Benefits: Comprehensive rewards package, flexible working hours, and commitment to inclusion.
- Other info: Collaborative environment with opportunities for professional growth.
- Why this job: Join a dynamic team and make an impact in the world of data science.
- Qualifications: Experience with relational databases and CI/CD practices required.
The predicted salary is between 50000 - 65000 £ per year.
A leading data science company in Greater London seeks a professional in Customer Data Science. In this role, you will design and deliver analytical Python solutions, utilizing machine learning techniques and collaborating across teams.
Candidates should have experience with relational databases and CI/CD practices.
The position offers a comprehensive rewards package, flexible working hours, and a commitment to diversity and inclusion.
Production-Grade Data Science Engineer employer: dunnhumby
Contact Detail:
dunnhumby Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production-Grade Data Science Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your analytical Python solutions and machine learning projects. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of relational databases and CI/CD practices. Be ready to discuss how you've applied these in past roles, as it’ll show you’re the right fit for the team.
✨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 a better chance at landing that dream job.
We think you need these skills to ace Production-Grade Data Science Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with analytical Python solutions and machine learning techniques. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how you can contribute to our team. Let us know about your experience with relational databases and CI/CD practices.
Showcase Team Collaboration: Since this role involves collaborating across teams, share examples of how you've worked effectively with others in past projects. We love seeing candidates who can communicate and collaborate well!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at dunnhumby
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially in relation to data science. Be prepared to discuss your previous projects and how you've used Python to solve real-world problems. Practising coding challenges can also help you feel more confident.
✨Showcase Your Machine Learning Knowledge
Familiarise yourself with various machine learning techniques and be ready to explain them clearly. Think of examples where you've applied these techniques in past roles. This will demonstrate your practical experience and understanding of the subject.
✨Understand CI/CD Practices
Since the role involves CI/CD practices, make sure you can talk about your experience with continuous integration and deployment. Be ready to discuss tools you've used and how they improved your workflow or project outcomes.
✨Emphasise Collaboration Skills
This position requires collaboration across teams, so highlight your teamwork experiences. Prepare examples of how you've worked with others to achieve a common goal, and be ready to discuss how you handle feedback and differing opinions.