Quantitative Analyst (Finance) AI Model Evaluation

You will help evaluate AI systems that handle quantitative finance problems. Your work will focus on whether model responses are accurate, well reasoned, and useful for real finance tasks.

You will create assessment rubrics and structured feedback for finance questions and collaborate with AI researchers and finance experts on evaluation methods and benchmarks.

• Evaluate responses about stochastic modeling, derivatives pricing, statistical arbitrage, and risk quantification.
• Create rubrics for options pricing, Monte Carlo simulation, factor model construction, and backtesting tasks.
• Provide feedback that helps improve AI model performance on quantitative finance work.
• Collaborate with AI researchers and finance specialists on training methods, evaluation strategies, and benchmarks.

This is a remote, part-time contract role with a commitment of 20 or more hours per week. The initial project lasts about one month and may be extended based on project needs.

Prior AI training experience is not required. The role does require strong quantitative finance knowledge and the ability to explain technical judgments clearly in written English.

• Pay: The provided role details do not specify a compensation rate.
• Schedule: 20 or more hours per week for about one month, with possible extension.
• Location: Open worldwide and fully remote.
• Experience: At least two years in quantitative finance, such as quantitative trading, quantitative research, financial engineering, or risk modeling.
• Technical knowledge: Stochastic calculus, statistical modeling, and derivatives pricing theory.
• Programming: Experience with Python, R, or C++.
• Language: Excellent written English.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind systems that write, reason, and answer questions. Contributors review model responses, create evaluation standards, and provide feedback so AI systems become more accurate and useful; specialized expertise matters because it helps measure performance in complex fields such as quantitative finance.