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Binance Accelerator Program - Applied Data Scientist

AsiaRemoteFull-time

AI Summary

Under supervision, the role works on AI systems powering Binance AI Products and agentic trading features. They own deliverables, run experiments, and ship code for LLM-powered pipelines, prompts, and evaluation tools in a production-like setting.

About this role

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

About Binance Accelerator Program
Binance Accelerator Program is a concise fixed-term program designed for Early Career Talent to have an immersive experience in the rapidly expanding Web3 space. You will be given the opportunity to experience life at Binance and understand what goes on behind the scenes of the worlds’ leading blockchain ecosystem. Alongside your job, there will also be a focus on networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about BAP Program HERE
Who may apply
Current university students and recent graduates

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.

    • Skills

    Agent EvaluationAgentic Coding ToolsAI-native DevelopmentAttention MechanismsBenchmarkingClaude CodeCursorData PipelinesGitHub Copilot WorkspaceLLMsMarket FeedsModel EvaluationOn-chain DataPromptingPythonPython-based Strategy LogicRetrieval ArchitecturesSentiment Signals

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