AI-Driven Quant Trading Strategy Intern

Binance; United States; À distance; USD 60 000 - 90 000; Plein temps

Avantages offerts par ce poste

Remote-first

Competitive compensation

Career learning opportunities

Résumé du poste

Binance is building an AI-driven trading research program and seeks a Quantitative Trading Strategy Algorithm Intern. You will explore factor discovery, factor prediction, and trading system construction, using Python and ML/DL methods on multi-source data including market and on-chain data.

Ideal candidates are current Master’s or PhD students in CS/Math/Statistics/Financial Engineering/Physics, with strong quantitative foundations and commitment to stable weekly internship hours.

Qualifications

• Currently enrolled in a Master’s or PhD program with strong quantitative foundations.
• Interest in quantitative trading strategies and backtesting workflows.
• Experience with Python and ML/DL applied to finance data.

Responsabilités

• Participate in discovery, construction, and validation of trading factors from market and on-chain data.
• Design and optimize factor prediction models using ML/DL techniques.
• Contribute to design, backtesting, and validation of trading strategies and risk research.
• Help build the quantitative trading strategy pipeline from data to backtesting.
• Track frontier methods bridging traditional markets and on-chain assets.

Connaissances

Python

ML/DL methods

Quantitative trading

Time-series data

Factor mining

Formation

Master’s or PhD student

Fields: CS, Math, Statistics, Financial Eng, Physics

Outils

Python

Backtesting tools

Description du poste

Binance is building an AI-driven trading research program and seeks a Quantitative Trading Strategy Algorithm Intern. You will explore factor discovery, factor prediction, and trading system construction, using Python and ML/DL methods on multi-source data including market and on-chain data.

Ideal candidates are current Master’s or PhD students in CS/Math/Statistics/Financial Engineering/Physics, with strong quantitative foundations and commitment to stable weekly internship hours.