Quantitative Researcher

Acerca del puesto

Uncharted Network's trading edge is built in the research layer. As a Quantitative Researcher, you will design experiments, build hypotheses, and backtest signals across equity microstructure, cross-asset factor models, and on-chain market data — then work directly with the trading desk to ship winning strategies into production. Our research environment is close-knit: researchers, engineers, and traders work from the same data and share the same feedback loop, and your models are never more than a sprint away from running live capital. We care about rigour, reproducibility, and intellectual honesty above any single modelling technique.

Responsabilidades

• Generate, test, and iterate on quantitative trading signals across equities, crypto, and futures
• Build robust backtesting frameworks that account for transaction costs, market impact, and non-stationarity
• Collaborate with traders to translate research findings into deployable, live-tested strategies
• Develop factor models, microstructure studies, and statistical arbitrage frameworks
• Maintain a reproducible research codebase and produce clear write-ups for strategy review sessions
• Mentor junior researchers and contribute to the firm's growing internal knowledge base

Requisitos

• PhD or equivalent research depth in Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline
• Proficiency in Python for data analysis and modelling (pandas, NumPy, statsmodels, scikit-learn)
• Strong command of time-series analysis, probability theory, and statistical inference
• Experience working with high-frequency, alternative, or on-chain financial datasets
• Demonstrated ability to move from hypothesis to production-tested strategy with rigorous evaluation
• Comfortable communicating complex findings clearly to both technical and non-technical audiences

Sería ideal

• Experience with deep learning methods applied to financial time series (PyTorch, JAX)
• Familiarity with on-chain data pipelines and blockchain-native signals such as DEX order flow and on-chain positioning
• Knowledge of market microstructure literature and limit order book dynamics