Research Data Scientist NLP Financial Signals
## Responsibilities:
Research and develop quantitative trading strategies using NLU methods such as sentiment analysis, intent recognition, named-entity extraction on financial news, social media, and other text sources
Design and build machine-learning models to uncover predictive trading signals and perform exploratory data analysis on large, complex datasets
Apply mathematical techniques (probability, statistics, time-series analysis) to refine and strengthen trading models
Rigorously backtest strategies against historical data and iteratively optimise models to boost performance and curb risk
## Requirements:
Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Financial Engineering or a related discipline
Strong mathematical foundation: probability, statistics, linear algebra, time-series analysis and familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
Solid grasp of NLU techniques, including sentiment analysis, intent recognition, and named-entity recognition
Proficiency in Python or R, with hands-on experience in NLP libraries (SpaCy, NLTK, Transformers)
A passion for exploring undefined problem space in the fast changing crypto world
• *The crypto industry is evolving rapidly, offering new opportunities in blockchain, web3, and remote crypto roles — don’t miss your chance to be part of it.**
Apply Now
Apply Now
Research and develop quantitative trading strategies using NLU methods such as sentiment analysis, intent recognition, named-entity extraction on financial news, social media, and other text sources
Design and build machine-learning models to uncover predictive trading signals and perform exploratory data analysis on large, complex datasets
Apply mathematical techniques (probability, statistics, time-series analysis) to refine and strengthen trading models
Rigorously backtest strategies against historical data and iteratively optimise models to boost performance and curb risk
## Requirements:
Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Financial Engineering or a related discipline
Strong mathematical foundation: probability, statistics, linear algebra, time-series analysis and familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
Solid grasp of NLU techniques, including sentiment analysis, intent recognition, and named-entity recognition
Proficiency in Python or R, with hands-on experience in NLP libraries (SpaCy, NLTK, Transformers)
A passion for exploring undefined problem space in the fast changing crypto world
• *The crypto industry is evolving rapidly, offering new opportunities in blockchain, web3, and remote crypto roles — don’t miss your chance to be part of it.**
Apply Now
Apply Now