Data Scientist, NLP & Trading Strategies (Quantitative)

4 Hours ago • All levels
Research Development

Job Description

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.
Must Have:
  • Research and develop quantitative trading strategies using NLU methods.
  • Design and build machine-learning models for predictive signals.
  • Apply mathematical techniques (probability, statistics, time-series analysis).
  • Rigorously backtest and optimize strategies to boost performance.
  • Bachelor’s or Master’s degree in relevant fields (CS, Math, Stats, Fin. Eng.).
  • Strong mathematical foundation: probability, statistics, linear algebra, time-series analysis.
  • Familiarity with ML frameworks like Scikit-learn, TensorFlow, PyTorch.
  • Solid grasp of NLU techniques: sentiment analysis, intent recognition, named-entity recognition.
  • Proficiency in Python or R, with NLP libraries (SpaCy, NLTK, Transformers).
Perks:
  • 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)

Add these skills to join the top 1% applicants for this job

data-analytics
talent-acquisition
game-texts
mathematical
sentiment-analysis
data-science
scikit-learn
pytorch
python
tensorflow
linear-algebra

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_

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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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