Data Scientist (NLP)

Summary

Join the Binance Accelerator Program as an Early Career Talent and contribute to the crypto-currency revolution by utilizing NLP techniques, designing data models, and extracting insights from large textual datasets.

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

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
• Ensure data models are robust, scalable, and optimized for performance
• 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