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 Team Overview

We are looking for Quantitative Developer Interns to collaborate with our Research group. We are 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, risk and trading cost forecasts based on the signals to drive trading decisions. We maintain a friendly, team-oriented environment and place a high value on professionalism, attitude and initiative.


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.


As a Quantitative Developer Intern, you will be immersed in our research effort, working side-by-side with members of the quantitative development team. Our internship program combines theory, practice and technology and provides significant insights into quantitative investment management. You will work on high impact projects applying technologies including cloud, distributed and high-performance compute, numeric computation, data visualization and APIs to solve complex problems in finance and research.


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

>        Performing ad-hoc exploratory statistical analysis across multiple large complex data sets from a variety of structured and unstructured sources

>        Implementing performance improvements in our data analysis and numerical programming code

>        Running POCs to evaluate new technologies and libraries in the PyData ecosystem


>        Enrolled in an undergraduate or graduate program 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 and statistics, including linear regression and time-series analysis

>        Interest in financial markets (prior experience not required)

>        Excellent communication skills including data visualization

>        High energy and strong work ethic


Please include your resume and transcript(s) upon submission of your application.