Binance is hiring a
Data Scientist

Summary

The Binance Accelerator Program is a fixed-term program for Early Career Talent in the Web3 space. The role involves utilizing NLP techniques to analyze and extract insights from large textual datasets, developing and implementing NLP models, designing complex data models, performing exploratory data analysis, and more.

Requirements

• Proficient in designing, developing, and evaluating complex data models
• Familiarity with statistical analysis and machine learning frameworks
• Deep understanding of modern machine learning techniques and mathematical underpinning, such as classifications, neural networks, hyperparameter optimization, etc
• Strong knowledge and experience in NLP techniques and tools for analyzing and extracting insights from textual data
• Solid understanding and practical experience with deep learning architectures, including transformer models (e.g., BERT, GPT). Ability to implement and optimize these models for various tasks
• Proficiency in programming languages such as Python, R, or similar. Experience with libraries and frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn
• Demonstrated experience in handling severely imbalanced datasets. Knowledge of techniques and strategies to address imbalances in data
• Holds a Master's degree or higher in Computer Science, Data Science, Statistics, Mathematics, Computational Linguistics, or a related field. Current Master's or Ph.D. students are welcome to apply

Responsibilities

• Utilize NLP techniques to preprocess, analyze, and extract insights from large textual datasets
• Develop and implement NLP models to derive actionable insights and enhance business decision-making processes
• Design, develop, and evaluate complex data models to support statistical analysis, machine learning, and other data-driven tasks
• Perform data cleaning, transformation, and preprocessing to create high-quality datasets for analysis and modeling
• Conduct exploratory data analysis to uncover patterns, trends, and relationships within the data. Generate visualizations and summaries to communicate findings to stakeholders
• Develop and apply feature engineering techniques to create meaningful features that improve the performance of models. This includes deriving new features from raw data, selecting relevant features, and transforming existing features