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- Tasks: Evaluate dialogue across text and audio, ensuring top-notch data labelling.
- Company: Join Yellowcat, a dynamic team in Central London.
- Benefits: Earn £140 daily plus holiday pay, with weekly payments.
- Other info: On-site role, perfect for those seeking hands-on experience.
- Why this job: Make an impact in AI while honing your analytical skills.
- Qualifications: Fluency in Dutch and strong analytical abilities required.
The predicted salary is between 36400 - 36400 £ per year.
Yellowcat in Greater London is seeking Dutch speaking Researchers / Administrators for a 6-month temporary position. The role involves evaluating dialogue on multiple data types including text and audio, delivering high-quality labelling to meet KPIs, and diagnosing operational issues.
Fluency in Dutch and strong analytical skills are essential. The position requires on-site work 5 days a week with a daily rate of £140 plus holiday allowance, paid weekly through PAYE.
Dutch ML Data Labeler – AI Project (6m, Central London) employer: Yellowcat
Contact Detail:
Yellowcat Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Dutch ML Data Labeler – AI Project (6m, Central London)
✨Tip Number 1
Get to know the company! Research Yellowcat and their projects. Understanding their values and goals can help you tailor your approach during interviews and show that you're genuinely interested in the role.
✨Tip Number 2
Practice makes perfect! Brush up on your Dutch language skills and analytical abilities. You might be asked to demonstrate these during the interview, so being prepared will give you a leg up.
✨Tip Number 3
Network like a pro! Connect with current or former employees of Yellowcat on LinkedIn. A friendly chat can provide insider tips and maybe even a referral, which can make all the difference.
✨Tip Number 4
Apply through our website! We make it super easy for you to submit your application directly. Plus, it shows you're serious about the opportunity and want to be part of our team.
We think you need these skills to ace Dutch ML Data Labeler – AI Project (6m, Central London)
Some tips for your application 🫡
Show Off Your Dutch Skills: Since fluency in Dutch is a must, make sure to highlight your language skills right at the top of your application. We want to see how well you can communicate in Dutch, so don’t hold back!
Demonstrate Your Analytical Prowess: This role needs strong analytical skills, so include examples from your past experiences where you've used these skills effectively. We love seeing how you’ve tackled problems and delivered results!
Tailor Your Application: Make your application stand out by tailoring it specifically to this role. Mention your experience with data labelling or any relevant projects that align with what we’re looking for. We appreciate when candidates take the time to connect their background to our needs.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Yellowcat
✨Brush Up on Your Dutch
Since fluency in Dutch is a must for this role, make sure you’re comfortable speaking and writing in the language. Practise common phrases and technical vocabulary related to data labelling and analysis to show your proficiency during the interview.
✨Know Your Data Types
Familiarise yourself with different data types, especially text and audio, as they are crucial for the role. Be prepared to discuss how you would evaluate and label these data types effectively, and think of examples from your past experiences that demonstrate your analytical skills.
✨Understand KPIs and Quality Standards
Research what KPIs might be relevant for a data labelling role. Be ready to talk about how you would ensure high-quality labelling and meet those KPIs. Showing that you understand the importance of quality in data handling will impress your interviewers.
✨Prepare for Operational Challenges
Think about potential operational issues that could arise in data labelling and how you would diagnose and resolve them. Having a few scenarios in mind will help you demonstrate your problem-solving skills and readiness for the challenges of the job.