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Binance | Taipei, Taiwan |
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Research Data Scientist, NLP & Financial Signals
Taiwan, Taipei / Asia / Hong Kong / Australia, Brisbane / Australia, Melbourne / Australia, Sydney / New Zealand, Auckland / New Zealand, Wellington / Argentina, Buenos Aires / Czech Republic, Prague / Georgia, Tbilisi / Hungary, Budapest / Italy, Milan / Philippines, Manila / Poland, Krakow
Engineering – Data Science/AI /
Full-time: Remote /
Remote
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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 280 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.
Responsibilities:
- 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
Requirements:
- 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 space in the fast changing crypto world
Why Binance
• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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What does NLP Engineer do?
A Natural Language Processing (NLP) Engineer is a professional who designs and develops software systems that can understand and generate human language
They typically work with machine learning and artificial intelligence (AI) technologies to create NLP solutions for various applications, including chatbots, virtual assistants, search engines, sentiment analysis, and language translation
NLP engineers play a critical role in developing software systems that can understand and generate human language, enabling us to interact with technology in more natural and intuitive ways
The role of an NLP Engineer includes:
- Data preprocessing: NLP engineers are responsible for cleaning, normalizing, and transforming raw text data into a format that can be used by machine learning algorithms.
- Feature engineering: NLP engineers design and develop features that are relevant to the NLP problem they are trying to solve. They extract features such as word frequency, part-of-speech tagging, and sentiment analysis.
- Building machine learning models: NLP engineers use machine learning algorithms such as deep learning, neural networks, and decision trees to train models that can understand and generate human language.
- Evaluating and improving models: NLP engineers evaluate the performance of their models using various metrics such as accuracy, recall, and F1 score. They also use techniques such as cross-validation and hyperparameter tuning to improve model performance.
- Integration: NLP engineers integrate their NLP solutions into larger software systems or platforms, such as chatbots or virtual assistants.