Pooria Amini

• Designed and coded a full experimental pipeline in Python to test whether LLMs can replicate human decision‐making in discrete choice experiments, integrating four provider APIs (Claude, Gemini, Groq/Llama, OpenAI‐compatible). • Ran a full scale automated pilot of 144 AI respondents across 2 regions, 4 survey blocks and 3 prompt conditions, producing 864 structured choice observations.

• Built weekly Python based price forecasts for graphics cards and processors, modelling USD/IRR exchange rate volatility impact on the business. • Built Python based models to structure and analyze data under highly volatile condition • Ran inflation‐based scenario analyses to support purchasing, sales and inventory decisions under volatile market conditions. • Delivered weekly forecast reports and monthly presentations as part of a five person analyst team. • Tracked performance data, flagging risks and o

• Supported regulatory compliance, quality assurance and risk management activities. • Coordinated with project managers to resolve technical and organisational challenges. • Built and managed monthly shift schedules to optimise workflow.

M.Sc. Environment and Resource Management, focused on sustainability, water resource management and renewable energy. Master's thesis: "Testing Large Language Models in Environmental Discrete Choice Experiments" designed and coded a Python based experimental pipeline testing whether LLMs can repl