What are the responsibilities and job description for the High Frequency Trading Quant Researcher (Equities) position at Quanta Search?
Our client, a global prop trading firm, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by their unparalleled access to a wide range of publicly available data sources.
They are growing and looking to hire an Equities Quant Analyst
Role/Responsibilities:
Perform rigorous and innovative research to discover systematic anomalies in the equities
market
End-to-end development, including alpha idea generation, data processing, strategy backtesting,
optimization, and production implementation
Identify and evaluate new datasets for stock return prediction
Maintain and improve portfolio trading in a production environment
Contribute to the analysis framework for scalable research
Requirements:
MS or PhD in mathematics, statistics, machine learning, computer science, engineering,
quantitative finance, or economics
3 years of work experience in systematic alpha research in cash equities, with exposures to
statistical arbitrage or alternative data research
Fluency in data science practices, e.g., feature engineering. Experience with machine learning is
a plus
Experience with signal blending and portfolio construction
Demonstrated proficiency in Python
Highly motivated, willing to take ownership of his/her work
Collaborative mindset with strong independent research abilities
Commitment to the highest ethical standards
They are growing and looking to hire an Equities Quant Analyst
Role/Responsibilities:
Perform rigorous and innovative research to discover systematic anomalies in the equities
market
End-to-end development, including alpha idea generation, data processing, strategy backtesting,
optimization, and production implementation
Identify and evaluate new datasets for stock return prediction
Maintain and improve portfolio trading in a production environment
Contribute to the analysis framework for scalable research
Requirements:
MS or PhD in mathematics, statistics, machine learning, computer science, engineering,
quantitative finance, or economics
3 years of work experience in systematic alpha research in cash equities, with exposures to
statistical arbitrage or alternative data research
Fluency in data science practices, e.g., feature engineering. Experience with machine learning is
a plus
Experience with signal blending and portfolio construction
Demonstrated proficiency in Python
Highly motivated, willing to take ownership of his/her work
Collaborative mindset with strong independent research abilities
Commitment to the highest ethical standards