Data Scientist - AI Agent Development

As a Data Scientist at Binance, you will design, fine-tune, and deploy Large Language Models (LLMs) for AI agent applications, implement Retrieval-Augmented Generation (RAG) pipelines, develop traditional Optical Character Recognition (OCR) systems, and collaborate with cross-functional teams to identify use cases. Additionally, you will conduct data preprocessing, feature engineering, and model evaluation to ensure strong performance of AI solutions.

To apply for the Data Scientist role at Binance, candidates should have a Master’s or Ph.D. in Computer Science, Data Science, or Machine Learning. Strong experience with LLM fine-tuning and proficiency in traditional OCR techniques are essential. Additionally, skills in Python programming and familiarity with cloud platforms such as AWS, Azure, or GCP are required.

In the Data Scientist position at Binance, you will integrate OCR outputs with LLM-based AI agents to create seamless workflows for information processing. By developing traditional OCR systems for document digitization and text extraction, you will enable AI agents to accurately process and respond to user queries based on extracted text.

Data Scientists at Binance will work on unique and fast-paced projects involving the development of AI agents, fine-tuning Large Language Models, and enhancing OCR systems. You'll engage in research and development, collaborating with world-class talent on innovative solutions, all while contributing to the advancement of the global blockchain ecosystem.

Binance is committed to fostering career growth and continuous learning for its employees. As a Data Scientist, you will have the autonomy to tackle challenging projects, collaborate with diverse teams, and stay updated on the latest technologies in AI and blockchain, all of which contribute to your professional development in a dynamic environment.

When answering this question, highlight specific projects where you fine-tuned LLMs, mentioning the types of models used (like GPT or BERT) and any frameworks such as Hugging Face or OpenAI APIs. Share challenges faced and how you overcame them, emphasizing the impact on project outcomes.

Discuss your standard steps for data preprocessing, such as cleaning data, handling missing values, normalization, and feature engineering. It's important to mention tools and libraries you are familiar with, showcasing your systematic approach to preparing data for effective model training.

Explain your evaluation metrics such as accuracy, precision, recall, F1-score, or AUC-ROC. Discuss the importance of cross-validation and any specific tools you use to track model performance over time, demonstrating your analytical skills and commitment to model optimization.

In response to this question, explain the OCR tools you’ve used, such as PaddleOCR models, and specific projects where you implemented these technologies. Highlight any challenges you faced and how you improved OCR accuracy and efficiency in your applications.

Mention the cloud platforms you're familiar with, such as AWS, Azure, or GCP, and describe the types of machine learning models you deployed to these environments. Discuss specific configurations, services used, and how you ensured scalability and efficiency in deployments.

Share your strategies for keeping up with industry trends, such as following research papers, attending conferences, or participating in online courses. It's important to express your passion for continuous learning and curiosity about how these advancements can be applied to your work.

Prepare a concise example where you addressed a real-world problem using data science. Outline the problem, methods applied (like statistical analysis or machine learning), and the positive impact your solution had on the project or organization.

Emphasize your belief in teamwork and how you’ve effectively collaborated with cross-functional teams on prior projects. Provide examples of successful collaborations, where knowledge was shared between engineers, product managers, and other stakeholders to achieve common goals.

Discuss your approach to feature engineering, emphasizing the significance of selecting relevant features that contribute to model performance. Share examples of techniques you’ve used, such as creating new features, using domain knowledge, or applying feature selection algorithms.

In your response, show awareness of challenges such as regulatory compliance, data privacy, or integrating complex systems in a fast-paced environment. Discuss how you would address these challenges with innovative solutions and adaptability, highlighting your proactive mindset.