Junior Quantitative Researcher
6.0/10
Binance
Not specified
Remote
junior
14 days ago
aicryptofintechweb3PythonMachine LearningAIStatistics
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Description
What you'll do
- •**Signal research and construction.** Develop, test, and productionize predictive signals across asset classes using a combination of statistical methods, machine learning, and AI agent–driven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.
- •**Root cause analysis (RCA).** Investigate model behavior, signal decay, PnL attribution, and unexpected trading outcomes. Build tools — including agentic ones — that accelerate diagnosis and shorten the loop between observation and fix.
- •**Market microstructure research.** Study order book dynamics, execution costs, liquidity, and venue behavior to inform both signal design and execution strategy.
- •**AI agent infrastructure for research.** Help design and extend internal agentic systems that automate parts of the research pipeline — data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.
- •**Collaborate broadly.** Work closely with traders, engineers, and other researchers to turn ideas into live, monitored strategies.
Conditions
- •Shape the future with the world’s leading blockchain ecosystem.
- •Collaborate with world-class talent in a user-centric global organization with a flat structure.
- •Tackle unique, fast-paced projects with autonomy in an innovative environment.
- •Thrive in a results-driven workplace with opportunities for career growth and continuous learning.
- •Competitive salary and company benefits.
- •Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team).
Requirements
Nice to Have
- •Prior internship or research experience at a hedge fund, prop trading firm, market maker, bank, or fintech.
- •Exposure to market microstructure, limit order books, or high-frequency data.
- •Experience with backtesting frameworks, time-series analysis, or causal inference.
- •Familiarity with low-latency systems, or large-scale data infrastructure.
- •Publications, open-source contributions, or trading competition results.
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