Quantitative Researcher
About Zeviq
Zeviq Technologies is an AI-native research and software company building AI agents for systematic trading across futures, equities, options, and perpetuals. Our research combines statistical modeling, machine learning, and deep market knowledge to look for durable, testable edges. We operate at the intersection of mathematical rigor and engineering precision: every idea is tested, validated, and understood before it moves forward.
We are looking for a Quantitative Researcher to develop and refine systematic trading strategies across the full research lifecycle, from hypothesis generation and data analysis through model development, out-of-sample validation, and paper trading. Your work will directly shape the research our agents and platform produce.
Responsibilities
- Identify, develop, and validate systematic trading signals across equity index, metals, energy, and rates futures
- Build and maintain backtesting frameworks, statistical models, and research infrastructure
- Analyze large-scale market microstructure data to find signals that hold up out-of-sample
- Collaborate with the engineering team to turn validated research into tested, deterministic code
- Develop risk models, execution models, and position sizing frameworks
- Review and improve strategies through rigorous out-of-sample and paper-trading analysis
Requirements
- MS or PhD in Mathematics, Statistics, Computer Science, Physics, or a related quantitative field
- 1–2 years of hands-on quantitative trading or research experience
- Strong proficiency in Python (NumPy, pandas, statsmodels, scikit-learn)
- Deep understanding of statistical inference, time-series analysis, and applied machine learning
- Experience with futures markets, market microstructure, or order flow analysis
- Rigorous research methodology — you validate ideas against out-of-sample data and quantify uncertainty before trusting results
- Clear written and verbal communication of complex quantitative findings
Nice to have
- Experience with tick-level data, limit order book analysis, or execution modeling
- Published research in quantitative finance, statistics, or machine learning
- Proficiency in C++ or Rust for performance-critical components
- Familiarity with institutional market data platforms such as Databento, Bloomberg, or Refinitiv
What we offer
- Competitive base compensation
- Hands-on work with AI research agents and our research platform from day one
- A small, focused team where your research directly shapes what we build and research
- Access to high-quality data, infrastructure, and research tooling