Our Research group is a collaborative, data-driven, intellectually rigorous team responsible for coming up with investment ideas, codifying those ideas into signals, back-testing the signals, and producing return and risk forecasts based on the signals to drive trading decisions. Our Quantitative Development team within Research is responsible for the tools, APIs, libraries and software engineering techniques to support faster generation, evaluation and productionization of investment ideas.
Our Quantitative Developer Interns are immersed in our research effort, working side-by-side with members of the Research, Quantitative Development and Research Systems teams. Our internship program provides experience with exploratory data analysis, production data pipelines, high-performance computing, research APIs, and the overall research model development lifecycle. Interns also gain significant exposure to the quantitative investment management process. Interns projects may deliver new numerical functions, evaluate new technologies, improve computational performance, or provide new ways to access to data through APIs or visualization. Typical responsibilities include:
> Writing Python and R code to support the investment research production processes
> Designing and creating software to enhance our data science technology stack
> Implementing performance improvements in our data analysis and numerical programming code
> Running POCs to evaluate new technologies and libraries in the PyData ecosystem
> Performing ad-hoc exploratory statistical analysis across multiple large complex data sets from a variety of structured and unstructured sources
> Enrolled in undergraduate or graduate degree from a top educational institution in a technical field, such as data science, applied mathematics, economics, engineering or computer science. Expected degree completion within a year of the internship
> Demonstrated academic success
> Strong analytical, quantitative, programming and problem solving skills
> Experience writing Python or R code as part of a large data-intensive project
> Knowledge of OOP paradigms, data structures, and numerical algorithms
> Understanding of probability, statistics, linear regression, and time-series analysis
> Excellent communication skills including data visualization
> Interest in financial markets
Please include a cover letter detailing your short and long-term career goals as well as your resume and transcript(s) upon submission of your application.