Senior Quantitative Developer

As a Quantitative Developer, your role involves contributing to the development of our upcoming Research data platform by utilizing open-source, cloud, and distributed computing technologies. You'll engage in impactful projects that rapidly gain adoption, instigating change within the team.

Key Responsibilities:

• Develop and maintain Python libraries supporting investment research production processes.
• Design and implement software to enhance our data science technology stack.
• Create financial data APIs and numerical APIs.
• Apply cloud and distributed computing technologies.
• Implement performance enhancements in data analysis and numerical programming code.
• Conduct Proof of Concepts (POCs) to assess new technologies and libraries in the PyData ecosystem.
• Collaborate with software engineers to design feeds for new data sources from third-party vendors.
• Propose and lead the implementation of major components or features in our data science platform.
• Mentor, train, and provide technical guidance to junior team members in design and coding standards.
• Contribute to other projects based on experience and interest.

Qualifications:

• Bachelor's or Master's degree in computer science.
• Strong analytical and problem-solving skills.
• Proficient programming skills in Python, with experience in implementing production-grade Python code.
• Knowledge of OOP paradigms, data structures, and numerical algorithms.
• Familiarity with data storage (RDBMS, S3, columnar databases, NOSQL databases).
• Experience in distributed computing (Spark, Dask, or HPC).
• Understanding or interest in 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.
• High energy as well as a strong work ethic.

Additional experience with any of the following would be valuable:

• Hadoop, Spark, Kafka, and related technologies.
• Unix/Linux system tools and environment.
• Basic familiarity with unit testing, continuous integration, DevOps, containerization.
• Interactive data visualization and dashboards.