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Binance | Taipei, Taiwan |
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Data Scientist, Quantitative Trading (NLU/NLP)
Asia / Taiwan, Taipei / Thailand, Bangkok / Australia, Brisbane / Australia, Melbourne / Australia, Sydney
Engineering ā Data Science/AI /
Full-time: Remote /
Remote
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Binance is a leading global blockchain ecosystem behind the worldās largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 250 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
This role sits at the intersection of quantitative finance, machine learning, and natural-language understanding. Youāll leverage NLU techniquesālike sentiment and intent analysisāto mine news and social media for trading signals, build and mathematically refine predictive models, and rigorously backtest and optimize your strategies to drive data-driven investment decisions.
Responsibilities:
- Research and develop quantitative trading strategies using NLU methodsāsentiment analysis, intent recognition, named-entity extractionāon 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:
- Bachelorās or Masterās degree in Computer Science, Mathematics, Statistics, Financial Engineering or a related discipline
- 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)
- Strong mathematical foundationāprobability, statistics, linear algebra, time-series analysisāand familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
- Excellent analytical and problem-solving abilities, with strong communication and teamwork skills
- Bilingual English/Mandarin is required to be able to coordinate withĀ overseasĀ partners andĀ stakeholders.
Why Binance
ā¢ Shape the future with the worldās leading blockchain ecosystem
ā¢ Collaborate with world-class talent in a user-centric global organization with a flat structure
ā¢ Tackle unique, fast-paced projects with autonomy in an innovative environment
ā¢ Thrive in a results-driven workplace with opportunities for career growth and continuous learning
ā¢ Competitive salary and company benefits
ā¢ Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.
By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.
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What does a data scientist in web3 do?
A data scientist in web3 is a type of data scientist who focuses on working with data related to the development of web-based technologies and applications that are part of the larger web3 ecosystem
This can include working with data from decentralized applications (DApps), blockchain networks, and other types of distributed and decentralized systems
In general, a data scientist in web3 is responsible for using data analysis and machine learning techniques to help organizations and individuals understand, interpret, and make decisions based on the data generated by these systems
Some specific tasks that a data scientist in web3 might be involved in include developing predictive models, conducting research, and creating data visualizations.