Data Scientist
atBinance
May 20
Binance is a leading global blockchain ecosystem behind the world’s 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 RoleThis role sits at the intersection of quantitative finance, machine learning, and natural-language understanding. You’ll leverage NLU techniques—like sentiment and intent analysis—to 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 methods—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 optimize 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
- 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 foundation—probability, statistics, linear algebra, time-series analysis—and familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
- Excellent analytical and problem-solving abilities, with strong communication and teamwork skills
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