Open Binance Accelerator Program - Data Engineer & Analytic
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Requirements
• Proficient in Python, with hands-on experience in making API calls using libraries such as requests and handling data formats like json. You will be expected to develop, test, and optimize scripts that interface with various data services and internal tools efficiently. • Strong data processing skills in PySpark and SQL (Hive), enabling you to extract, transform, and load large data workloads in distributed environments to support timely and accurate data insights. • Solid foundation in software development fundamentals, including version control (Git) • Excellent problem-solving abilities and a logical mindset, allowing you to analyze challenges critically, identify root causes, and devise effective solutions. • Eagerness to learn and strong communication skills, empowering you to engage effectively with team members, share knowledge, and take initiative in owning tasks from conception to completion, adapting quickly to new tools and technologies as needed. • Experience with AI or evaluation-related projects, whether personal, academic, or professional, demonstrating familiarity with AI concepts, data labeling, model performance assessment, or related methodologies. • Understanding of large language models (LLM) and issues such as hallucination, providing context for your work in AI evaluation and helping improve model reliability and trustworthiness. • Hands-on experience with tools such as Label Studio (for data annotation), Airflow (for workflow orchestration)
Responsibilities
• Develop and maintain large datasets for analysis purposes. • Perform complex analytics to derive insights from the company's data assets. • Collaborate with cross-functional teams to understand business needs and translate them into actionable projects. • Implement machine learning models or algorithms as required by project specifications, using tools like Python, R, TensorFlow, PyTorch etc. • Ensure the quality of data through cleaning, validation, normalization, transformation processes where necessary to prepare datasets for analysis and model training. • Monitor performance metrics regularly to assess models' accuracy and efficiency in real time scenarios when applicable. • Document all stages from raw dataset collection to final reporting with comprehensive logs that detail methodologies used at each step of the data pipeline lifecycle, including ETL processes if relevant. ✅
Benefits
• 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)