Arrowstreet Capital
Quantitative Developer, Associate
Arrowstreet Capital, Boston, Massachusetts, us, 02298
Team Overview
We are looking for Quantitative Developers to join 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, you will help build our next-generation Research data platform leveraging open-source, cloud and distributed computing technologies. You will work on high-impact projects that are quickly adopted and drive change across the team.
Responsibilities
Your responsibilities are expected to grow in line with your experience and abilities. Depending on your competitive advantages, typical responsibilities may include:
Writing and maintaining Python and R code that supports 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
Staying up to date on the PyData ecosystem and evaluating new libraries and tools
Working with software engineers to design feeds for new data sources from third-party vendors
Qualifications
An undergraduate or graduate degree from an educational institution in computer science with a quantitative application such as mathematics and/or finance, or vice versa – a quantitative degree with a computer science application
Demonstrated professional or academic success (recent graduates are encouraged to apply)
Strong analytical, quantitative, and problem solving skills
Experience implementing production-grade Python code for a data analytic business, preferably in investment management
Expert programming skills in Python with pandas and numpy
Expertise in OOP paradigms, data structures, and numerical algorithms
Understanding of probability and statistics, including linear regression and time-series analysis
Curiosity and a willingness to learn new technologies
Interest in financial markets (prior experience not required)
Excellent communication skills, including data visualization
High energy and strong work ethic
In addition, experience with any of the following would be valuable:
Some experience programming in R with tidyverse packages
High-performance computing
Distributed computing
Hadoop, Spark, Kafka, and related technologies
SQL
Unix/Linux system tools and environment
Basic familiarity with unit testing, continuous integration, DevOps, containerization
Interactive data visualization and dashboards
#J-18808-Ljbffr
We are looking for Quantitative Developers to join 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, you will help build our next-generation Research data platform leveraging open-source, cloud and distributed computing technologies. You will work on high-impact projects that are quickly adopted and drive change across the team.
Responsibilities
Your responsibilities are expected to grow in line with your experience and abilities. Depending on your competitive advantages, typical responsibilities may include:
Writing and maintaining Python and R code that supports 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
Staying up to date on the PyData ecosystem and evaluating new libraries and tools
Working with software engineers to design feeds for new data sources from third-party vendors
Qualifications
An undergraduate or graduate degree from an educational institution in computer science with a quantitative application such as mathematics and/or finance, or vice versa – a quantitative degree with a computer science application
Demonstrated professional or academic success (recent graduates are encouraged to apply)
Strong analytical, quantitative, and problem solving skills
Experience implementing production-grade Python code for a data analytic business, preferably in investment management
Expert programming skills in Python with pandas and numpy
Expertise in OOP paradigms, data structures, and numerical algorithms
Understanding of probability and statistics, including linear regression and time-series analysis
Curiosity and a willingness to learn new technologies
Interest in financial markets (prior experience not required)
Excellent communication skills, including data visualization
High energy and strong work ethic
In addition, experience with any of the following would be valuable:
Some experience programming in R with tidyverse packages
High-performance computing
Distributed computing
Hadoop, Spark, Kafka, and related technologies
SQL
Unix/Linux system tools and environment
Basic familiarity with unit testing, continuous integration, DevOps, containerization
Interactive data visualization and dashboards
#J-18808-Ljbffr