Product Manager

Overview

Verantos (https://verantos.com/) is the global leader in high-validity real-world evidence (RWE) for life sciences. By incorporating robust clinical narrative data, artificial intelligence technology, and measured validity, Verantos is the first company to generate research-grade evidence at scale across therapeutic areas. Verantos integrates heterogeneous real-world data sources and generates evidence with the accuracy necessary for market access, HEOR, medical affairs, and regulatory use.

Our product strategy is built on three modular, connected pieces — the Curation Platform (raw data to structured, validated variables), Pragmatic Registry data sets (high validity, enriched datasets), and the Evidence Platform (standardized data to evidence). Each delivers standalone value; the combination is the differentiator, and each platform's usage accelerates the others. Some of the largest biopharma companies in the world are Verantos customers.

As Senior Product Manager, you will own the product that turns the richest, most difficult clinical data into trusted, structured evidence. You will support teams to build reference standards, train and validate AI models, and measure accuracy with full transparency aligned with FDA guidance. You will set the vision and roadmap as a hands-on, AI-native product manager.

Responsibilities

• Own the vision, strategy, and roadmap, sequencing delivery across your product area
• Partner with the users and cross-functional collaborators — annotators, annotation supervisors, clinical subject-matter experts, AI + data engineers, and customer success— to translate their needs into a coherent, scalable product that produces research-grade, defensible outputs
• Champion the unifying product principles across every feature: transparency at every step, knowledge that compounds over time, “AI does the work; experts decide,” and design for scale through configurability rather than custom engineering
• Work in an AI-native, prototype-first way — using LLMs (e.g., Claude, ChatGPT, Gemini) and modern building tools (e.g., v0, Claude Cowork/Code, OpenAI Codex) to spec and pressure-test ideas yourself, then ship thin slices, measure accuracy and throughput with users, and improve in tight loops rather than long waterfall cycles
• Define and instrument success metrics — extraction accuracy (precision/recall/F1), inter- annotator agreement, throughput, and cost-per-dataset reduction — and use them to drive prioritization
• Manage the product release process and develop launch materials in collaboration with marketing and sales

Qualifications