Pioneer Talent Program - Applied Data Scientist

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. ### Responsibilities - Design and implement production-grade LLM pipelines powering Binance AI Products and next-generation agentic trading features — including multi-step reasoning agents, tool-selection frameworks, and autonomous workflow execution across spot, perpetual, and on-chain markets - Continuously improve agent capabilities in understanding, reasoning, tool selection, and action execution — optimizing simultaneously for intelligence, latency, and reliability under high-frequency trading constraints - Build and maintain evaluation frameworks for reasoning model outputs in crypto contexts — covering market analysis accuracy, agent decision quality, hallucination detection, and adversarial robustness against prompt injection in financial workflows - Apply test-time scaling techniques — chain-of-thought, self-consistency, process reward models — to push agent reasoning quality in ambiguous, fast-moving market conditions - Architect AI system components with rigorous attention to inference latency, throughput, and cost efficiency — leveraging serving frameworks such as vLLM and TensorRT-LLM — in a zero-downtime, 24/7 trading environment - Integrate on-chain data sources, wallet intelligence, and crypto market signals into LLM-powered analytical pipelines — building the data layer that makes Binance's agents genuinely crypto-native - Partner with research scientists to translate experimental findings into production-grade agentic solutions with clear performance benchmarks ### Qualifications - Bachelor's or Master's degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or related technical field - 0–5 years of industry or research experience in applied ML or AI engineering - Strong Python programming skills ; Equally important: demonstrated comfort with vibe coding — using AI-assisted development tools fluidly as core part of your workflow - Demonstrated hands-on experience with LLMs — prompt engineering, post-training, or end-to-end LLM application development - Familiarity with multi-agent system design — task decomposition, tool use via MCP, memory management, parallel agent execution, and inter-agent communication - Strong analytical thinking and problem decomposition; comfortable operating under ambiguity in fast-moving environments