Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Dive into NLP projects, classifying English text and enhancing algorithms with user feedback.
- Company: Join a cutting-edge tech company focused on innovative machine learning solutions.
- Benefits: Enjoy flexible work hours, remote options, and opportunities for professional growth.
- Why this job: Be part of a dynamic team shaping the future of language processing technology.
- Qualifications: Experience in Python, machine learning, and NLP techniques is a must.
- Other info: Ideal for those passionate about computational linguistics and real-world applications.
The predicted salary is between 36000 - 60000 £ per year.
NLP / Natural Language Processing Data Scientist
Machine Learning application to NLP or Natural Language Processing problems using NLP to classify English text, learn, process and respond using: Computational Linguistics, ACL, NIPS, Empirical Methods, Neural Networks, algorithms, Python, etc.
The ideal candidate will have experience in online or active learning, human in-the-loop systems, and algorithm improvement through user feedback from a Data Scientist or Machine Learning perspective.
#J-18808-Ljbffr
NLP / Natural Language Processing Data Scientist employer: Expert Employment
Contact Detail:
Expert Employment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land NLP / Natural Language Processing Data Scientist
✨Tip Number 1
Make sure to showcase your hands-on experience with NLP projects. Highlight any specific algorithms or models you've worked with, especially those related to classification and user feedback mechanisms.
✨Tip Number 2
Familiarize yourself with the latest research in NLP by reading papers from conferences like ACL and NIPS. Being able to discuss recent advancements can set you apart during interviews.
✨Tip Number 3
Demonstrate your proficiency in Python and relevant libraries such as TensorFlow or PyTorch. Consider building a small project that showcases your skills in computational linguistics and neural networks.
✨Tip Number 4
Engage with the data science community online. Participate in forums or contribute to open-source projects related to NLP. This not only builds your network but also shows your passion for the field.
We think you need these skills to ace NLP / Natural Language Processing Data Scientist
Some tips for your application 🫡
Understand the Role: Make sure you fully understand the requirements and responsibilities of the NLP Data Scientist position. Familiarize yourself with key concepts like Computational Linguistics, Neural Networks, and active learning.
Tailor Your CV: Customize your CV to highlight relevant experience in NLP, Machine Learning, and Python. Include specific projects or achievements that demonstrate your skills in these areas.
Craft a Strong Cover Letter: Write a compelling cover letter that explains why you're a great fit for the role. Mention your experience with human in-the-loop systems and how you've improved algorithms through user feedback.
Showcase Your Projects: If you have any personal or professional projects related to NLP or Machine Learning, be sure to mention them. Provide links to your GitHub or any relevant publications to showcase your work.
How to prepare for a job interview at Expert Employment
✨Showcase Your NLP Knowledge
Be prepared to discuss your experience with Natural Language Processing. Highlight specific projects where you've applied computational linguistics, neural networks, or algorithms to solve real-world problems.
✨Demonstrate Your Coding Skills
Since Python is a key requirement, make sure you can talk about your coding experience. Be ready to explain your thought process while solving coding challenges or algorithms related to NLP.
✨Discuss Active Learning Techniques
Familiarize yourself with online and active learning concepts. Be ready to share examples of how you've implemented these techniques in past projects, especially in human-in-the-loop systems.
✨Prepare for Algorithm Improvement Questions
Expect questions on how to improve algorithms based on user feedback. Think of specific instances where you enhanced a model's performance and be ready to discuss the metrics you used to measure success.
