Design and document systematic investment workflows (factor models, back-testing, portfolio construction, VaR, stress testing).
Develop evaluation criteria and benchmark solutions to assess AI performance on quantitative finance tasks.
Review and validate AI-generated outputs for statistical accuracy, modeling soundness, and risk logic.
Curate and structure high-quality financial and market data for model training and testing.
Provide feedback to improve AI reasoning in derivatives modeling, risk assessment, and alpha generation.
Requirements:
3+ years of experience as a Quantitative Analyst, Quantitative Researcher, Risk Manager, Systematic Portfolio Manager, or Derivatives Trader.
Strong background in statistics, probability, time-series analysis, and financial modeling.
Proficiency in Python and experience working with large financial datasets.
Hands-on experience with systematic strategies, back-testing frameworks, derivatives (options, futures, swaps), and risk models (VaR, stress testing).
High analytical mindset, attention to detail, and ability to clearly explain quantitative reasoning.
Benefits:
Flexible work arrangements
Skills & Requirements
Technical Skills
PythonStatisticsProbabilityTime-series analysisFinancial modelingAiBack-testingDerivativesRisk modelsMonte carlo simulationStatistical analysisAnalytical mindsetAttention to detailCommunicationQuantitative financeAi in finance