Position Description:

Implements quantitative strategies and portfolio analytics platforms, using R, Python, Git, subversion, and data science pipelines’ best practices in data extraction, transformation, and computing. Designs and builds quantitative research pipelines and frameworks. Implements strategies and updates into production environments. Creates and assists in creating marketing materials. Develops systematic and active investment strategies using quantitative research techniques.

Primary Responsibilities:

• Works with portfolio managers and research teams to provide aspects of product and portfolio management support associated with existing strategies and expected growth in customized solutions.

• Conducts and streamlines quantitative research on signals and factors that predict future stock returns, using data science techniques – large dataset manipulation, Web scraping, natural language processing, and econometrics and statistical techniques.

• Develops active/quantitative equity investment strategies based on statistical and mathematical techniques.

• Conducts research on signals/factors that assist in predicting future stock returns.

• Communicates results with internal and external stakeholders.

• Mentors interns and provides guidance on projects.

• Conducts quantitative analyses of information affecting investment programs of public or private institutions.

• Informs investment decisions by analyzing financial information to forecast business, industry, or economic conditions.

• Prepares plans of action for investment using financial analyses.

• Evaluates and compares the relative quality of various securities in a given industry.

Education and Experience:

Master’s degree (or foreign education equivalent) in Financial Mathematics, Mathematics, Finance, Computational Finance, Statistics, Economics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) designing, creating, analyzing, and validating quantitative equity investment strategies (alpha and risk models), using advanced statistical, mathematical, and machine learning techniques — multivariate regression, non-parametric statistics, Bayesian statistics, time series analysis, principal component analysis (PCA), and random forests.

• DE conducting and streamlining quantitative research on signals and factors which predict future stock returns, using data science techniques including, large dataset manipulation, Webscraping, natural language processing, and econometrics and statistical techniques — macro and micro economic reasoning, multivariate regression, and statistical machine learning.

• DE implementing quantitative strategies and portfolio analytics platforms, using R, Python, git, subversion, and data science pipelines’ best practices in data extraction, transformation, and computing.

• DE utilizing economics, linear algebra, numerical analysis, and mathematical optimizations to interpret portfolio construction options and analyze impact on portfolio performance profiles –returns, risks, exposures, robustness and sensitivities.

[Expertise may be gained during graduate degree program].

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