Jacobs Levy Equity Management
Senior Quantitative Equity Researcher
Jacobs Levy Equity Management, Florham Park, New Jersey, us, 07932
Candidates must include a CV/resume when applying.
Jacobs Levy Equity Management, located in Florham Park, NJ is seeking a Senior Quantitative Equity Researcher to join our research team. The team is responsible for researching all aspects of the investment process from data processing and alpha modeling through to portfolio optimization. Our researchers work collaboratively to contribute to our firm’s leading edge, innovative investment process. We seek people who are passionate about equity investment and motivated to outperform the market.
Building on the pioneering research of founders Bruce Jacobs and Ken Levy, Jacobs Levy Equity Management has developed a unique, multidimensional, dynamic approach to investing that combines human insight and intuition, finance and behavioral theory, leading-edge quantitative and statistical methods, and over 30 years of proprietary research.
Responsibilities include:
Conducting exploratory statistical data analysis
Empirical research into U.S. and global equity market inefficiencies
Reviewing financial literature
Developing new and improving existing investment models by identifying novel investment ideas and innovative data sources
Ideal candidates will look to combine creative insights with research to make sound investment decisions.
Requirements include:
PhD in Finance, Economics, Statistics, or related quantitative discipline
At least 3 years of empirical equity research experience
Familiarity with fundamental, expectational and market data. Experience with alternative data is a plus
Solid knowledge of asset pricing literature
Ability to think independently with good economic intuition and demonstrated record of original research
Strong programming skills (Python, R, Julia, C++, etc.), preferably experienced with large datasets and also familiar with parallel programming
An understanding of Machine Learning, Natural Language Processing, and AI Engineering is a plus
Ability to work collaboratively across departments and to explain challenging technical concepts
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Jacobs Levy Equity Management, located in Florham Park, NJ is seeking a Senior Quantitative Equity Researcher to join our research team. The team is responsible for researching all aspects of the investment process from data processing and alpha modeling through to portfolio optimization. Our researchers work collaboratively to contribute to our firm’s leading edge, innovative investment process. We seek people who are passionate about equity investment and motivated to outperform the market.
Building on the pioneering research of founders Bruce Jacobs and Ken Levy, Jacobs Levy Equity Management has developed a unique, multidimensional, dynamic approach to investing that combines human insight and intuition, finance and behavioral theory, leading-edge quantitative and statistical methods, and over 30 years of proprietary research.
Responsibilities include:
Conducting exploratory statistical data analysis
Empirical research into U.S. and global equity market inefficiencies
Reviewing financial literature
Developing new and improving existing investment models by identifying novel investment ideas and innovative data sources
Ideal candidates will look to combine creative insights with research to make sound investment decisions.
Requirements include:
PhD in Finance, Economics, Statistics, or related quantitative discipline
At least 3 years of empirical equity research experience
Familiarity with fundamental, expectational and market data. Experience with alternative data is a plus
Solid knowledge of asset pricing literature
Ability to think independently with good economic intuition and demonstrated record of original research
Strong programming skills (Python, R, Julia, C++, etc.), preferably experienced with large datasets and also familiar with parallel programming
An understanding of Machine Learning, Natural Language Processing, and AI Engineering is a plus
Ability to work collaboratively across departments and to explain challenging technical concepts
#J-18808-Ljbffr