• To help with this, we’re looking for a research intern with training in observational causal inference and causal machine learning who’s excited to apply their skills to real-world problems in financial advising. • Apply observational causal inference methods with clear identification strategies to isolate conversational variables that causally influence outcomes. • Engineer structured features from unstructured transcript data (e.g., advisor talk ratio, sentiment, interruptions, trust markers, hesitations) using LLMs, embeddings, and NLP. • Analyze large-scale anonymized transcript datasets. • Strengthen the methodological rigor of our research design and analysis. • Contribute to research that pushes the financial advising industry forward. • Develop a sustainable process and reusable causal model that the team can operate independently after the internship, ensuring continuity and scalability of insights.