Quantitative Researcher

Solo Quant builds predictive analytics for futures markets. The platform analyses how instruments reacted to past economic releases and, before the next release, reports directional bias, confidence, consistency and expected volatility for each instrument. Our proprietary trading and risk model is deterministic and works only from historical market data.

The first phase covers GC, NQ and ES around CPI, NFP and FOMC. Our clients are prop firms, hedge funds, institutional traders and research teams. The product and the team are at an early stage.

The team is small, and each hire owns part of the product. We look for self-motivated people who drive their own work without close supervision. We use Claude Code and other AI tools every day for coding, research and analysis, and we expect you to check AI output as critically as your own work.

We usually work between 8:00 and 20:00 CET (CEST in summer).

• Calibrate the model's parameters and features, and measure each change against the current version.
• Improve how the model calculates directional bias, confidence scores, consistency and volatility profiles.
• Validate every change out of sample and test it for look-ahead bias, data snooping and overfitting.
• Track how each report performed after the release, find where the model drifts and correct it.
• Turn feedback from the Trading & Risk Analyst into tested model changes.
• Run event studies on CPI, NFP, FOMC and other releases, measuring the surprise against consensus, across different time windows and market regimes.
• Build and extend research pipelines and backtests in our in-house engine.
• Prepare futures data, including contract rolls, trading sessions and time zones.
• Look for new event types, instruments and features that improve the model.
• Report findings to the product and engineering teams and to clients, including changes that did not work.

• Junior: recent graduates and current MSc or PhD students.
• Mid: experience in quant research, trading or data science on market data, with at least one research project that went into production.
• Degree in mathematics, physics, statistics, computer science, econometrics or a related field.
• Thorough knowledge of statistics, including hypothesis testing, regression and time-series analysis, and experience calibrating or validating statistical models.
• Python for data analysis (pandas, NumPy) and SQL.
• Clean, reproducible research code kept under version control (Git).
• Experience with financial time-series data from work, research or personal projects.
• Interest in financial markets and macroeconomics.
• You review your own results critically before presenting them.
• You work in a structured way, pay attention to detail and can work independently.
• Good written and spoken English.

• Experience with C++, C#, Java or Rust.
• Experience with futures, options or intraday and tick data.
• Experience with economic calendar data, including consensus forecasts and revisions.
• Background in event studies, econometrics or market microstructure.
• Experience building or using backtesting frameworks.
• Knowledge of Bayesian methods, machine learning or regime detection.
• Experience with Claude Code or other AI development tools.

• Compensation based on experience.
• Access to Claude Code and other AI tools.
• Mentoring matched to your experience level.
• Responsibility for your own area of the product.
• Career progression based on performance.
• Training and networking opportunities.