资深量化研究员（多因子策略方向）

About the role

This role involves independent research and development of multi-factor strategies based on large-scale data, including data cleaning, feature extraction, and factor mining for price-volume factors, fundamental factors, and alternative data. You will construct and optimize models using linear and non-linear approaches, perform backtesting and attribution analysis, build investment portfolios based on factor model outputs, design risk constraints, manage portfolio rebalancing, optimize trading costs, and conduct performance attribution. You will also participate in daily management of multi-factor strategies in live trading, tracking strategy performance, iterating weights, and estimating risks to ensure strategy stability and sustainability.

Key responsibilities

• Conduct independent research and development of multi-factor strategies based on large-scale data, including data cleaning, feature extraction, and factor mining for price-volume factors, fundamental factors, and alternative data
• Apply linear and non-linear models for strategy modeling, backtesting, and attribution analysis
• Construct investment portfolios based on factor model outputs and design risk constraints
• Manage portfolio rebalancing, optimize trading costs, and conduct performance attribution within a risk budget framework
• Participate in daily management of multi-factor strategies in live trading
• Track strategy performance, iterate weights, and estimate risks to ensure strategy stability and sustainability

About you

• Bachelor's degree or above in mathematics, statistics, physics, computer science, or financial engineering
• Solid mathematical foundation in probability theory, linear algebra, stochastic processes, and optimization theory
• Good understanding of financial theory including asset pricing, factor models, and risk management
• Proficient in Python for data processing, modeling, and backtesting (pandas / numpy / sklearn / PyTorch)
• Demonstrated ability to independently complete factor mining and linear/non-linear model research
• Proficient in using AI-assisted research tools (such as Claude, Cursor, Copilot) to enhance research efficiency
• Experience with cross-sectional strategies is preferred