Binance - Data Scientist, NLP & Trading Strategies (Quantitative)
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Requirements
• 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 • Why Binance • Candidate Privacy Notice
Responsibilities
• Analyze and interpret complex datasets to identify trends relevant for trading strategies. • Develop machine learning models using Python/R that can predict market movements based on historical data. • Collaborate with the quantitative team to refine existing algorithms or create new ones tailored to specific financial instruments. • Conduct thorough backtesting of proposed NLP and AI tools for trading strategies against live markets, ensuring robustness before deployment. • Stay updated on market news and regulatory changes that could impact the development and implementation of data science projects in finance. • Communicate findings to stakeholders through reports or presentations using visualization software like Tableau or Power BI. • Participate in code reviews, contribute to open source machine learning libraries used within Binance's technology stack when applicable. • Maintain and improve the quality of data pipelines that feed into AI models by cleaning datasets and handling missing values appropriately. • Monitor model performance over time using dashboards or custom scripts; report any significant deviations to team leads for further investigation. • Engage with Binance's internal knowledge base, document best practices, and contribute articles on data science methodologies in finance when possible. • Attend regular meetings with the quantitative team to discuss project progress, challenges faced, and brainstorm solutions collaboratively.
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