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We're running a paid study on how financial data and workflows can be accurately modeled inside reinforcement learning environments. By building realistic accounting scenarios, we can better train artificial intelligence systems to handle complex financial decision-making. Your domain expertise will directly inform the structure and logic of these simulated worlds.
During this remote session, you will interact with our reinforcement learning platform tailored for financial workflows. You will be asked to map out common finance and accounting scenarios, detailing the variables, constraints, and potential outcomes. The platform will guide you through building these simulated worlds while you explain your logical choices. You will also provide feedback on the intuitive feel and accuracy of the modeling tools provided.
We are looking for experienced finance and accounting professionals with a strong grasp of complex financial modeling. We welcome certified public accountants, financial analysts, quantitative analysts, and corporate finance managers. A technical background or prior experience with artificial intelligence platforms is helpful but not strictly required.
Interact with a reinforcement learning platform designed for the financial sector
Map out realistic finance and accounting scenarios using platform tools
Define variables, constraints, and logic for complex financial workflows
Provide feedback on the accuracy and usability of the scenario builder
Active professional in finance, accounting, or quantitative analysis
Strong understanding of complex financial workflows and decision logic
Comfortable interacting with new technical platforms and modeling tools
Ability to clearly articulate accounting principles and scenario constraints
$90 per hour
Terac is building the world's largest pool of vetted human experts for AI. Researchers, AI labs, and product teams use Terac to recruit, screen, and pay study participants across industries, languages, and skill sets.
Learn more at terac.com or on YouTube at @jointerac.
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