Binance Accelerator Program - Applied Data Scientist at Binance

About the Role This internship is part of Tech Seeds 2026 — our program to identify and develop AI-native young talent. As an Applied Data Scientist young talent, you will take on a hands-on builder role at the intersection of AI research and production systems, working alongside full-time algorithm engineers on LLM applications and AI system architecture from day one. This is not a shadowing role. You will own deliverables, run experiments, and ship code that matters.   What You'll Do Support the design and development of LLM-powered pipelines and AI application components Build and test prompt engineering strategies, retrieval-augmented generation (RAG) setups, or agent workflows Assist in model evaluation — designing metrics, running benchmarks, and interpreting results Analyze system performance and identify opportunities for improvement in latency, accuracy, or cost Use AI coding tools as a core part of your workflow — not as a shortcut, but as a force multiplier Collaborate with senior engineers and researchers to understand production AI system architecture What We're Looking For Currently pursuing Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field Expected graduation in 2026 or 2027 Solid Python programming skills and comfort with data manipulation (Pandas, NumPy) Basic understanding of machine learning concepts and at least one deep learning framework (PyTorch preferred) Curiosity about LLMs and generative AI — hands-on experimentation is a strong signal Ability to break down complex problems and work through them systematically Available for a 6-month full-time commitment Bonus Points Personal projects, coursework, or competition experience involving LLMs or generative AI Familiarity with tools like LangChain, OpenAI API, or Hugging Face Experience with Git and collaborative development workflows What Makes You a Fit for Tech Seeds 2026 You've already been experimenting with AI tools on your own — not because you had to, but because you were curious. You learn fast and want to build things that work in the real world.