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.
