Quantitative Researcher

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.

#J-18808-Ljbffr