• Use LLMs (ChatGPT, Claude, etc.) to analyze, categorize, and score datasets across various use cases. • Design and develop automated workflows to pipe data into LLM models and retrieve structured outputs. • Work with APIs, data pipelines, and automation tools to streamline LLM processing. • Build and maintain scalable processes for ongoing data ingestion, transformation, and evaluation. • Validate and refine AI outputs to ensure accuracy, consistency, and reliability. • Collaborate with cross-functional teams (Product, Engineering, Ops) to integrate AI-driven insights into business processes. • Develop documentation, frameworks, and best practices for AI data analysis and scoring processes. • Monitor model performance, troubleshoot issues, and propose improvements.

• Partner with stakeholders to define KPIs and analytical needs across product usage, customer journeys, and business performance. • Analyze data from trials, intakes, sales, therapy engagement, and marketing campaigns to uncover trends and opportunities. • Deliver clear, actionable dashboards and reports using Power BI and other tools. • Collaborate with data engineering on ETL pipelines, data modeling, and semantic layer design. • Document metric definitions, data sources, and assumptions to promote trust and transparency. • Support product managers with insights from Amplitude, including funnel analysis, retention, and user behavior. • Ensure compliance with HIPAA and data governance standards, including PHI/PII handling and access controls.

• Ensure that data flows smoothly from source to destination so that it can be processed • Utilize strong database skills to work with large, complex data sets to extract insights • Filter and cleanse unstructured (or ambiguous) data into usable data sets that can be analyzed to extract insights and improve business processes • Identify new internal and external data sources to support analytics initiatives and work with appropriate partners to absorb the data into new or existing data infrastructure • Build tools for automating repetitive asks so that bandwidth can be freed for analytics • Collaborate with program managers and business analysts to help them come up with actionable, high-impact insights across product lines and functions • Work closely with top management to prioritize information and analytic needs

• Multi-agent architecture for forming a Data & Analytics squad • Standardized validation methodology for LLMs on complex tasks • Methodology for software development with AIs and LLMs • Impact assessment of using Data & Analytics multi-agent systems on productivity; case study in the investments area