Research Data Scientist, NLP & Financial Signals: Binance

This is a Full-time Remote position across multiple global locations, including:

Asia/Australia: Taipei (Taiwan), Hong Kong, Brisbane, Melbourne, Sydney, Auckland, Wellington, Manila (Philippines).

Europe/South America: Buenos Aires (Argentina), Prague (Czech Republic), Tbilisi (Georgia), Budapest (Hungary), Milan (Italy), Krakow (Poland).

As a Data Scientist focusing on Quantitative Trading NLP at Binance, you will be leveraging Natural Language Understanding (NLU) techniques—such as sentiment analysis, intent recognition, and named-entity extraction—on financial news, social media, and other text streams to develop and refine algorithmic trading strategies in the crypto world. Your primary function will be to bridge text data analysis with quantitative trading.

Responsibilities
Research and develop quantitative trading strategies using NLU methods 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 optimize models to boost performance and curb risk.

Requirements
A Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Financial Engineering, or a related discipline.

Strong mathematical foundation: probability, statistics, linear algebra, and time-series analysis.

Solid grasp of NLU techniques (sentiment analysis, intent recognition, named-entity recognition).

Proficiency in Python or R, with hands-on experience in NLP libraries (SpaCy, NLTK, Transformers).

Familiarity with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.

A passion for exploring the undefined problem space in the fast-changing crypto world.