Quantitative Researcher in Natural Language Processing (NLP)
Imagine beating the efficient market hypothesis with the full big data toolset
G-Research is a leading quantitative research and technology company. By using the latest scientific techniques, we produce world-beating predictive research and build advanced technology to analyse the world’s data.
We use the latest scientific techniques and advanced data analysis methods to discover the undiscovered. Our researchers are free to explore ideas, finding patterns in large, noisy and real-world data sets to predict the movements in global financial markets.
You will join a team of quantitative researchers working on very large data sets, applying the latest techniques for entity recognition and sentiment extraction, with the goal of identifying features of real time text feeds that can we used to predict future behaviour of financial markets.
Who are we looking for?
- The ideal candidate 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 and Text Classification.
- Experience working with existing NLP and deep learning libraries (word embeddings, spaCy, CoreNLP, NLTK, PyTorch / TensorFlow / keras, etc.).
- Familiarity with the many recent exciting advances in NLP (pre-trained Language models such as BERT, contextualised word embeddings such as ELMo, Attention and novel neural network architectures, etc.).
- You will have an interest in applying mathematical and NLP concepts to real world financial problems, and implementing theoretical insights as working code.
- You will have, or be working towards gaining, a Masters or PhD degree in a highly quantitative subject (mathematics, statistics, computer science, physics or engineering). PhDs in NLP or Computational Linguistics are especially desirable.
- Publications at leading NLP and ML conferences such as NIPS, ICML, EMNLP, ACL and COLING is 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?
- Highly competitive compensation plus annual discretionary bonus
- Informal dress code and excellent work/life balance
- Comprehensive healthcare and life assurance
- 25 days holiday
- Contributory pension scheme
- Cycle-to-work scheme
- Subsidised gym membership
- Monthly company events
- Central London office close to 5 stations and 6 tube lines
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