Pioneer Talent Program - Applied Data Scientist (FTE)

Confirmed live in the last 24 hours

Binance

Asia

On-site

Posted April 20, 2026

Job Description

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 230 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 the Pioneer Talent Program

Are you an early-career professional ready to kickstart your career in the dynamic world of blockchain? Binance’s Pioneer Talent Program is designed to nurture ambitious individuals like you. The program offers full-time roles where participants will gain hands-on expertise via a tailored training curriculum and personalized 1:1 mentorship from industry leaders. Dive behind the scenes of the world’s leading blockchain ecosystem while building the skills to thrive in Web3. Launch your career with purpose—join us in shaping the future of digital innovation.

Who May Apply

Candidates with up to 5 years of relevant work experience

About the Role

We are seeking an Applied Data Scientist to join our Algorithm team — a hands-on builder at the intersection of LLM systems, agentic AI, and crypto-native intelligence.

You will design, build, and improve AI agents that understand markets, reason over on-chain data, and take actions at speed for hundreds of millions of users — across reasoning models that plan before they act, multi-agent systems that orchestrate complex crypto workflows, and test-time scaling that pushes inference-time intelligence to its limits.

You build for real environments: designing agent architectures, improving performance across quality and latency, developing evaluation methods for open-ended tasks, and turning research ideas into production.

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