Data Scientist
Research Data Scientist role in Binance's Accelerator Program, focusing on building and evaluating data-driven models for blockchain and crypto assets using Python, SQL, machine learning, statistical analysis, and big‑data tools.
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 with senior scientists on research problems at the frontier of LLM reasoning, post-training methodology, and agentic AI — applied to global crypto markets. Your work has a direct path to production systems serving hundreds of millions of users, and where findings warrant it, a clear path to external publication.
You run experiments, implement ideas from recent research, synthesize papers into hypotheses, and work with engineers to understand how research translates to real systems.
This is not a purely literature-review or passive research role. You are expected to think independently and generate insight.
Who may apply
Current university students and recent graduates
Responsibilities
Contribute to the design and execution of experiments in reasoning model training, post-training alignment, test-time scaling, or systematic model evaluation — with a focus on applications in financial and crypto-native contexts
Review and synthesize recent academic literature at NeurIPS, ICML, ICLR, and ACL — tracking developments in reasoning, alignment, and agentic AI to inform and sharpen ongoing research directions
Implement model variants, training procedures including RLVR-based approaches, and evaluation protocols using PyTorch and the Hugging Face ecosystem
Track and log experiments systematically using Weights & Biases or equivalent — maintaining reproducibility standards throughout
Explore the intersection of LLM reasoning and crypto-native data: on-chain signals, m
Posted June 24, 2026