What are the responsibilities and job description for the Quantitative Researcher position at StreetID?
We are a dynamic and fast-paced hedge fund leveraging data-driven strategies to generate alpha in global financial markets. Our team of technologists, quantitative researchers, and portfolio managers work collaboratively to translate cutting-edge research into production-ready trading models and systems. We are seeking a highly motivated Quantitative Analyst/Developer to join our growing technology and research group.
This role is ideal for candidates with a passion for data engineering, financial modeling, and software development. You will play a key role in building tools and frameworks that directly support quantitative research, strategy development, and live trading.
Key Responsibilities:
- Design, develop, and maintain robust data pipelines to ingest, clean, and process large-scale financial datasets using Python, with an emphasis on the Pandas library for high-performance data manipulation.
- Collaborate with quantitative researchers to implement and optimize research tools, statistical models, and backtesting frameworks.
- Work on the development of analytics and visualization dashboards to support model performance analysis, trade monitoring, and risk reporting.
- Optimize and refactor existing codebases for efficiency, scalability, and maintainability, ensuring robust data integrity and reproducibility of research results.
- Write clean, well-tested, and production-ready Python code with a focus on numerical stability, performance, and low-latency data access.
- Integrate third-party financial data vendors and APIs (Bloomberg, Refinitiv, FactSet, etc.) into the research infrastructure.
- Support live trading and strategy deployment pipelines by ensuring smooth data flow and minimal downtime.
- Collaborate with DevOps and engineering teams to implement CI/CD pipelines, containerization (Docker), and cloud-based compute workflows where appropriate.
- Stay current with new technologies, libraries, and best practices in data science, financial engineering, and Python-based software development.
Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, Physics, Statistics, or a related quantitative discipline.
- Experience in a quantitative developer, data engineer, or similar role, ideally within a hedge fund, trading firm, or financial institution.
- Extensive hands-on experience with the Pandas library for data manipulation, time-series analysis, and performance tuning.
- Strong proficiency in Python, with knowledge of its scientific computing stack (NumPy, SciPy, Matplotlib, Seaborn, etc.).
- Familiarity with software engineering principles: version control (Git), code reviews, testing frameworks, and continuous integration.
- Experience handling large, complex datasets and working with structured and unstructured data sources.
- Solid understanding of financial instruments (equities, derivatives, FX, fixed income) and market data.
- Excellent problem-solving skills, attention to detail, and the ability to work independently as well as in a collaborative team environment.