You will join a team of NLP experts and quantitative researchers working on very large text corpora, applying the latest techniques for entity recognition, sentiment analysis and topic extraction, with the goal of identifying features of real time text feeds that can we used to predict the future behaviour of financial markets.
Who are we looking for?
As an ideal candidate, you will be an expert in Natural Language Processing through academic or industry experience (or both).
You will have experience applying machine learning and deep learning methods to a range of NLP-related tasks, such as Named Entity Recognition, Entity Linking, Sentiment Analysis, Knowledge Graphs, MultiLingual Text and Topic Extraction.
You will have experience working with existing NLP and deep learning frameworks such as word embeddings, spaCy, CoreNLP, NLTK, PyTorch, TensorFlow and Keras.
You will be familiar with the many recent exciting advances in NLP such as pre-trained language models, contextualised word embeddings, attention and novel neural network architectures.
You will have an interest in applying NLP concepts to real world financial data, and implementing theoretical insights as working code.
You will have, or be working towards gaining, a Masters or PhD degree in NLP, Computational Linguistics or a related quantitative subject such as machine learning, computer science, mathematics, statistics, physics or engineering.
Publications at leading NLP conferences such as ACL and EMNLP, and ML conferences such as NeurIPS and ICML are highly desirable.
Previous financial experience is not required, although interest in finance and the motivation to rapidly learn more is a prerequisite for working here.
Why should you apply?