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- Tasks: Build analytical Python solutions and develop reusable packages for global products.
- Company: Leading data science company in Greater London with an inclusive culture.
- Benefits: Flexible working hours and a supportive work environment.
- Why this job: Make an impact by creating visual dashboards and leveraging machine learning techniques.
- Qualifications: Experience in machine learning, data processing, CI/CD, and DevOps.
- Other info: Join a dynamic team with opportunities for growth and innovation.
The predicted salary is between 36000 - 60000 £ per year.
A leading data science company in Greater London seeks a professional to build analytical Python solutions. You will develop reusable Python packages and create visual dashboards that support global products.
We are looking for candidates with experience in machine learning techniques and data processing in big data environments, along with familiarity in CI/CD and DevOps.
Enjoy flexible working hours and an inclusive work culture.
Data Science Engineer: Production-Ready Python in London employer: Dunnhumby
Contact Detail:
Dunnhumby Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Engineer: Production-Ready Python in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the data science field on LinkedIn or at local meetups. We can’t stress enough how valuable personal connections can be in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those involving machine learning and data visualisation. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Practice coding challenges and be ready to discuss your experience with CI/CD and DevOps. We want you to feel confident and ready to impress!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Science Engineer: Production-Ready Python in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and any machine learning techniques you've used. We want to see how your skills align with the role, so don’t be shy about showcasing your 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. Be sure to mention your familiarity with CI/CD and DevOps, as these are key for us.
Showcase Your Projects: If you've worked on any analytical Python solutions or created visual dashboards, include them in your application. We love seeing real examples of your work, so don’t forget to link to your GitHub or portfolio!
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’re considered for the role. Plus, it’s super easy to do!
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 building analytical solutions. Be ready to discuss your experience with developing reusable packages and how you've implemented them in past projects.
✨Showcase Your Machine Learning Knowledge
Prepare to talk about specific machine learning techniques you've used. Have examples ready that demonstrate how you've applied these techniques in big data environments, as this will show your practical understanding.
✨Familiarise Yourself with CI/CD and DevOps
Since the role involves CI/CD and DevOps, be prepared to discuss your experience with these practices. Think of instances where you've integrated these methodologies into your workflow and how they improved your projects.
✨Emphasise Your Team Spirit
This company values an inclusive work culture, so highlight your ability to work collaboratively. Share examples of how you've contributed to team projects and supported your colleagues in achieving common goals.
