Responsibilities

Your responsibilities will evolve alongside your experience and capabilities. Depending on your strengths, typical responsibilities may include:

• Consolidating, organizing, and analyzing extensive datasets from diverse origins.
• Evaluating the quality of past and present data, identifying shortcomings, and proposing solutions.
• Conducting ad-hoc exploratory statistical analyses across numerous complex datasets sourced from structured and unstructured channels.
• Developing and maintaining production-level code directly contributing to the investment process.
• Investigating discernible trends in asset returns, risks, trading expenses, and other data pertinent to financial markets.
• Conducting research on portfolio construction utilizing our simulation tools.
• Collaborating with software engineers to devise data feeds for new sources from third-party providers.
• Contributing to data architecture decisions supporting the Research data platform.

Qualifications:

• Currently enrolled in or graduated from an undergraduate or graduate program in finance, mathematics, economics, or a closely-related discipline with a focus on quantitative and financial analysis.
• Demonstrated success in professional or academic pursuits (recent graduates are welcome).
• Proficiency in analytical, quantitative, and problem-solving skills.
• Familiarity with probability, statistics, linear regression, time-series analysis, linear algebra, calculus, optimization, and portfolio theory.
• Understanding of statistical applications in economics (including econometrics or regression analysis).
• Experience with statistical computing environments such as Python, Stata, R, or MATLAB.
• Experience in analyzing large datasets.
• Knowledge of finance, including equities and derivatives.
• Passion for financial markets.
• Strong communication skills, including proficiency in data visualization.
• High energy and a strong work ethic.

Additionally, the following would be advantageous:

• Solid understanding of empirical asset pricing in academic contexts.
• Familiarity with financial data products.
• Experience with stock market datasets.