Binance Accelerator Program - Applied Data Scientist

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means.   About Binance Accelerator Program Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program HERE.   Who may apply Current university students and recent graduates. *Terms of employment / engagement shall be subject to contract and local applicable laws

About the Role You'll work directly on the AI systems powering Binance AI Products and next-generation agentic trading features — alongside the full-time algorithm team, on real production challenges. You own deliverables, run experiments, and ship code that matters. You build components of AI systems (agents, pipelines, evaluation tools), debug real systems, and work with engineers to ship features that reach users. This is not a "watch and learn" program. You are expected to build, contribute, and ship. Responsibilities Contribute to the design and development of LLM-powered pipelines for agentic trading — including reasoning agent components, tool-use frameworks via MCP, and automated workflow execution across crypto markets. Build and evaluate prompt engineering strategies, test-time scaling approaches, and retrieval architectures for crypto-native data sources — on-chain data, market feeds, news, and sentiment signals. Design and run model evaluation experiments — defining quality metrics for agent reasoning in financial contexts, executing benchmarks, and synthesizing results into actionable findings. Analyze agent performance characteristics in live trading scenarios — covering decision accuracy, latency, reliability, and adversarial robustness. Apply AI-native development practices using agentic coding tools as a standard part of the engineering workflow — writing and executing Python code for strategy logic, data pipelines, and agent evaluation. Requirements Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field. Expected graduation in 2026 or 2027. Strong Python programming skills with demonstrated AI-native development practices — you use agentic coding tools (Claude Code, Cursor, GitHub Copilot Workspace) as a core part of your workflow. Foundational understanding of how large language models work — attention mechanisms, prompting, and the difference between standard generation and reasoning models. Structured problem-solving approach and ability to operate independently on defined tasks.