обновлено 10 дней назад
Systematic Credit Quantitative Researcher (Fintech)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Systematic Credit Quantitative Researcher (Fintech): Driving the research agenda and infrastructure for systematic credit market-making strategies with an accent on predictive signal generation and high-performance data pipelines. Focus on building scalable AI-driven backtesting platforms and collaborating with global trading desks to transition models into production.
Location: Must be based in Sao Paulo, Brazil
is a leading global financial institution providing a wide range of financial services.
What you will do
- Conduct rigorous statistical research to identify predictive signals across corporate bonds and credit ETFs.
- Architect and build a high-performance, research-grade data framework to establish a robust golden source for quantitative research.
- Design and maintain a scalable, AI-based platform for automated parameter tuning and backtesting.
- Collaborate with quantitative developers to build and scale simulation frameworks and production-grade analytics libraries.
- For VP-level candidates, lead architectural decisions and mentor junior researchers.
Requirements
- Master’s or PhD degree in a quantitative STEM discipline (Mathematics, Physics, Computer Science, Statistics, Operations Research, or Financial Engineering).
- 3+ years (Associate) or 6+ years (VP) of professional experience in quantitative research, financial engineering, or data science.
- Deep understanding of probability, statistics, linear algebra, and time-series analysis.
- Advanced proficiency in Python (Pandas, NumPy, SciPy, Scikit-Learn) with a software engineering mindset.
- Experience managing large-scale datasets using SQL and high-performance time-series databases like KDB+/Q.
Nice to have
- Direct experience researching systematic corporate bond or credit derivatives strategies.
- Hands-on experience with KDB+/Q or managing large-scale, tick-level financial datasets.
- Object-oriented programming skills in C++ or Java.
- Experience building self-service quantitative research platforms or APIs.
Culture & Benefits
- Opportunity to work at the intersection of financial engineering, machine learning, and high-performance computing.
- Collaboration with global desks in New York, London, and Hong Kong.
- Exposure to complex, fragmented OTC credit markets.
- Professional development through mentorship and high-impact research ownership.
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