Binance6mo ago

Data Scientist, NLP & Trading Strategies (Quantitative)

AsiaRemoteFull-time: Remotemid

Data ScienceData ScientistDataData & AI

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Quick Summary

Overview

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users.

Technical Tools

Data ScienceData ScientistDataData & AI

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

About the Role

As a Data Scientist focusing on Quantitative Trading NLP, you will leverage natural language understanding 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.

You’ll design and implement machine-learning models in Python, apply advanced mathematical and time-series analysis to uncover predictive signals, and rigorously backtest and optimize strategies to maximize returns while managing risk. Collaboration and clear communication across data science and trading teams are key to iteratively improving model performance and driving data-informed investment decisions.

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

At least 2 years of relevant experience in data science, machine learning, or natural language processing

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 spaces in the fast-changing crypto world