Junior Product Analyst, Hippocratic AI

• Bachelor’s degree (or equivalent experience) in Data Science, Statistics, Computer Science, or related field
• Exposure (including through coursework) to python (preferably), R, or another scripting language
• Experience analyzing data and creating reports
• Strong attention to detail, with a track record of accurately following instructions and processes
• Solid communication skills; able to convey findings to both technical and non-technical stakeholders
• Ability to stay organized and manage multiple tasks in a fast-paced environment

Desirable

• As a Junior Analyst at Hippocratic AI, you will be primarily responsible for data labeling (60-70%) and creating data reports (30-40%) to support the continuous improvement of our healthcare-focused LLM solutions
• In this role, you’ll help ensure our data is meticulously labeled and evaluated, perform second-level reviews of externally labeled data, and create reports based on labeled data and patient call outcomes
• Your work will directly influence the accuracy and reliability of Hippocratic AI’s solutions, helping maintain high standards of patient safety and experience
• You’ll collaborate with cross-functional teams—including engineering, product, and clinical experts—to troubleshoot data tasks, debug labeling complexities, and provide succinct insights
• While the majority of your time will be spent on labeling and adjudicating data, you’ll also contribute to ad-hoc reporting and analysis to optimize our internal processes and model performance
• Additionally, you will have extensive exposure to building a highly technical LLM product with lots of mentorship and growth opportunities
• Accurately label data to support model training and safety analysis
• Conduct second-level reviews/adjudications of nurse-produced labels to ensure accuracy and consistency
• Gather relevant datasets by running SQL queries or by running Python scripts
• Produce weekly data summaries and reports to assist senior team members in decision-making
• Track and document metrics or issues that arise during labeling, contributing to process improvements
• Work closely with engineering and product teams to clarify labeling guidelines and address edge-case scenarios
• Communicate findings clearly and succinctly, highlighting patterns or problems for cross-functional teams