Quantitative Risk Analyst
⚲ Amsterdam / Limassol
About the Role
As a Quantitative Risk Analyst, you will own the independent validation of trading strategies and the statistical framework behind capital allocation. Your work will help determine which strategies are ready for live trading, how much capital they can support, and when that capital should be reduced.
You will take an existing validation framework and develop it into a repeatable, automated function spanning backtest evaluation, release decisions, and live performance monitoring.
Reporting to the Head of Risk & Treasury, you will work directly with researchers, engineers, and trading desks. This is a hands-on role: you will develop the methodology, implement analytical workflows with AI coding tools, and defend your conclusions with evidence.
You will work on
Own the validation process for new trading strategies, documenting decisions and the capital allocation each result supports.
Assess statistical robustness, accounting for multiple testing, backtest overfitting, data leakage, and dependence between strategies.
Develop validation methods for large-scale research, including heavy-tailed results, related strategy families, and protected holdout datasets.
Build live monitoring to identify deviations from backtests, detect performance decay, and trigger strategy reviews or capital reductions.
Provide volatility, correlation, capacity, and expected shortfall estimates for trading limits, capital allocation, and stress scenarios.
Evaluate external trading teams and their track records before an initial allocation.
Extend the framework across crypto and traditional markets, including equities, futures, and options.
Write clear methodology notes and decision reports for research teams and senior stakeholders.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system
You might thrive in this role if you have
5+ years of experience in quantitative risk, quantitative research, or model validation at a trading firm, hedge fund, bank, or crypto fund.
A strong understanding of multiple testing and strategy selection, including deflated Sharpe ratio, probability of backtest overfitting, false discovery control, and the effective number of independent trials.
Practical knowledge of VaR, expected shortfall, drawdowns, volatility and correlation estimation, and stress testing.
Basic programming knowledge across Python, SQL, and Git, and the ability to build and verify analytical workflows using AI coding tools such as Claude Code or Cursor.
Experience working with large backtest datasets and financial time series.
The judgment to challenge research assumptions and turn findings into clear, actionable decisions.
Experience with crypto derivatives, market microstructure, or capacity modelling for mid-frequency and market-making strategies would be a plus.
Quantitative Risk Analyst
⚲ Amsterdam / Limassol
About the Role
As a Quantitative Risk Analyst, you will own the independent validation of trading strategies and the statistical framework behind capital allocation. Your work will help determine which strategies are ready for live trading, how much capital they can support, and when that capital should be reduced.
You will take an existing validation framework and develop it into a repeatable, automated function spanning backtest evaluation, release decisions, and live performance monitoring.
Reporting to the Head of Risk & Treasury, you will work directly with researchers, engineers, and trading desks. This is a hands-on role: you will develop the methodology, implement analytical workflows with AI coding tools, and defend your conclusions with evidence.
You will work on
Own the validation process for new trading strategies, documenting decisions and the capital allocation each result supports.
Assess statistical robustness, accounting for multiple testing, backtest overfitting, data leakage, and dependence between strategies.
Develop validation methods for large-scale research, including heavy-tailed results, related strategy families, and protected holdout datasets.
Build live monitoring to identify deviations from backtests, detect performance decay, and trigger strategy reviews or capital reductions.
Provide volatility, correlation, capacity, and expected shortfall estimates for trading limits, capital allocation, and stress scenarios.
Evaluate external trading teams and their track records before an initial allocation.
Extend the framework across crypto and traditional markets, including equities, futures, and options.
Write clear methodology notes and decision reports for research teams and senior stakeholders.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system
You might thrive in this role if you have
5+ years of experience in quantitative risk, quantitative research, or model validation at a trading firm, hedge fund, bank, or crypto fund.
A strong understanding of multiple testing and strategy selection, including deflated Sharpe ratio, probability of backtest overfitting, false discovery control, and the effective number of independent trials.
Practical knowledge of VaR, expected shortfall, drawdowns, volatility and correlation estimation, and stress testing.
Basic programming knowledge across Python, SQL, and Git, and the ability to build and verify analytical workflows using AI coding tools such as Claude Code or Cursor.
Experience working with large backtest datasets and financial time series.
The judgment to challenge research assumptions and turn findings into clear, actionable decisions.
Experience with crypto derivatives, market microstructure, or capacity modelling for mid-frequency and market-making strategies would be a plus.
Quantitative Risk Analyst
⚲ Amsterdam / Limassol
About the Role
As a Quantitative Risk Analyst, you will own the independent validation of trading strategies and the statistical framework behind capital allocation. Your work will help determine which strategies are ready for live trading, how much capital they can support, and when that capital should be reduced.
You will take an existing validation framework and develop it into a repeatable, automated function spanning backtest evaluation, release decisions, and live performance monitoring.
Reporting to the Head of Risk & Treasury, you will work directly with researchers, engineers, and trading desks. This is a hands-on role: you will develop the methodology, implement analytical workflows with AI coding tools, and defend your conclusions with evidence.
You will work on
Own the validation process for new trading strategies, documenting decisions and the capital allocation each result supports.
Assess statistical robustness, accounting for multiple testing, backtest overfitting, data leakage, and dependence between strategies.
Develop validation methods for large-scale research, including heavy-tailed results, related strategy families, and protected holdout datasets.
Build live monitoring to identify deviations from backtests, detect performance decay, and trigger strategy reviews or capital reductions.
Provide volatility, correlation, capacity, and expected shortfall estimates for trading limits, capital allocation, and stress scenarios.
Evaluate external trading teams and their track records before an initial allocation.
Extend the framework across crypto and traditional markets, including equities, futures, and options.
Write clear methodology notes and decision reports for research teams and senior stakeholders.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system
You might thrive in this role if you have
5+ years of experience in quantitative risk, quantitative research, or model validation at a trading firm, hedge fund, bank, or crypto fund.
A strong understanding of multiple testing and strategy selection, including deflated Sharpe ratio, probability of backtest overfitting, false discovery control, and the effective number of independent trials.
Practical knowledge of VaR, expected shortfall, drawdowns, volatility and correlation estimation, and stress testing.
Basic programming knowledge across Python, SQL, and Git, and the ability to build and verify analytical workflows using AI coding tools such as Claude Code or Cursor.
Experience working with large backtest datasets and financial time series.
The judgment to challenge research assumptions and turn findings into clear, actionable decisions.
Experience with crypto derivatives, market microstructure, or capacity modelling for mid-frequency and market-making strategies would be a plus.
