Quantitative Researcher

Arrowstreet Capital is a Boston-based systematic investment firm that manages global equity portfolios for institutional investors around the world. They are looking for Quantitative Researchers to join their Research group, responsible for developing investment ideas, implementing signals, and conducting data analysis to inform trading decisions.

What you'll do:

• Performing ad-hoc exploratory statistical analysis across large complex data sets from a variety of structured and unstructured sources
• Researching predictable patterns in asset returns, risks, trading costs and other data relevant to financial markets
• Writing and maintaining production-quality code used directly in the investment process
• Assessing the quality of historical and current data, diagnosing deficiencies, and prescribing fixes
• Performing portfolio construction research using our proprietary simulation capability
• Working with software engineers to design feeds for new data sources from third-party vendors
• Participating in data architecture decision-making to support the Research data platform
• Conducting research projects from initial stages through analysis, and presenting directly to the team and stakeholders

What they're looking for:

• Degree from an undergraduate or graduate educational institution in finance, mathematics, economics, or a closely-related discipline emphasizing quantitative or financial analysis
• 1-3 years of relevant experience recommended
• Understanding of probability, statistics, linear regression, time-series analysis, linear algebra, calculus, optimization and portfolio theory
• Knowledge of the application of statistics to economics (including econometrics or regression analysis)
• Experience with a statistical computing environment such as Python, Stata, R, or MATLAB
• Experience analyzing large data sets
• Understanding of finance (including equities and derivatives)
• Passion for financial markets
• Ability to communicate complex empirical research findings and conclusions clearly, including through effective use of data visualizations
• High energy and strong work ethic
• Good understanding of the academic field of empirical asset pricing
• Familiarity with financial data products