Return to jobs
As a skilled Linguistic Expert or a Sustainability Researcher, you will help to analyse text datasets of questions and answers, validate/comment on labelling provided by AI models, and collaborate to improve model performance.
Overview
Salary
£0
Location
London, EC1M 6BB
Expires
Expires at anytime
What will you be doing?
The ideal candidate will have a strong background in linguistics, and a keen interest in sustainability-related topics. Direct experience with NLP is a plus however is not mandatory. Responsibilities:
Text Analysis: Analyze large text datasets comprising questions and answers extracted from scientific research (SDGs topics). Label Validation: Validate and comment on labeling provided by AI models to ensure accuracy and relevance. Collaboration: Work closely with the Data Science team to identify patterns, trends, and areas for improvement in AI model performance. Research: Conduct research on linguistic theories, methodologies, and sustainability topics to enhance data analysis techniques. Model Evaluation: Contribute with human-led feedback to evaluate the effectiveness of AI models in handling linguistic nuances and sustainability-related content.
What are we looking for?
Qualifications:
Educational Background: Bachelor’s/Master’s/Ph.D. in Linguistics, Computational Linguistics, Sustainability Studies, or related fields. Experience:
Prior experience in linguistic analysis, NLP, or text mining is highly desirable. Familiarity with sustainability-related topics such as renewable energy, climate change, sustainable development goals (SDGs), etc.
Technical Skills:
Proficiency in programming languages such as Python, R, or Java for data analysis and scripting. Experience with NLP libraries/tools such as NLTK, SpaCy, Gensim, etc. Knowledge of machine learning algorithms and techniques is a plus.
Analytical Skills: Strong analytical and critical thinking skills to interpret complex textual data and draw meaningful insights. Communication: Excellent written and verbal communication skills to collaborate effectively with cross-functional teams and stakeholders. Detail-Oriented: Attention to detail and accuracy in data annotation, validation, and documentation. Problem-Solving: Ability to identify issues, troubleshoot problems, and propose effective solutions to improve model performance.
What difference will you make?
You will help to democratize knowledge and make complex topics easily understandable for everyone
