Binance
via Lever
Binance Accelerator Program - Applied Data Scientist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 147d ago
The date the source published, not the day we noticed it (2026-04-20). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Asia
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay A$1,675–2,250/mo
Middle 50% of 19 listings that do state pay — Operations · all levels · Asia · AUD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 42d ago · last seen just now
The listing
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
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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.
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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.
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Design and run model evaluation experiments — defining quality metrics for agent reasoning in financial contexts, executing benchmarks, and synthesizing results into actionable findings.
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Analyze agent performance characteristics in live trading scenarios — covering decision accuracy, latency, reliability, and adversarial robustness.
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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
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Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field.
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Expected graduation in 2026 or 2027.
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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.
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Foundational understanding of how large language models work — attention mechanisms, prompting, and the difference between standard generation and reasoning models.
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Structured problem-solving approach and ability to operate independently on defined tasks.