6-12+ years in Revenue Operations, Solutions Engineering, Software Engineering, or Product Management with strong AI application experience.
Proficient in Claude Code with practical experience deploying AI solutions into production environments.
Familiar with AI and automation tools such as Clay, Microsoft Copilot, and Salesforce.
Exceptional root-cause analysis and discovery skills.
Strong process redesign capabilities to align tools with actual workflows.
Ability to thrive in a fast-paced, high-growth setting.
Responsibilities
Embed within Commercial teams to conduct deep discovery and root-cause analysis.
Design and implement AI solutions that directly address root causes, rather than just automating broken processes.
Manage solutions throughout their lifecycle: from development to deployment and user training.
Enhance Commercial team's AI skills through enablement and best practices sharing.
Facilitate hackathons to foster creativity and prototype development within teams.
Cultivate strong collaborative relations with Engineering, particularly GTM Systems team.
Develop and maintain a prioritized roadmap, effectively communicating it to all relevant Commercial teams.
Benefits
Opportunities for professional development and skills training.
Engagement with innovative technologies and practices in AI and automation.
Collaborative and dynamic work environment focused on high-growth strategies.
Potential for creative input in internal projects and initiatives.
Full Job Description
What You Will Do
Embed within Commercial teams to run deep discovery and root-cause analysis: map how work actually gets done, ask probing questions, and identify the highest-leverage friction and manual work worth solving
Design and build AI solutions that resolve the root cause rather than bolting automation onto a broken process
Own solutions end to end: build, deploy into hosted environments, roll out, train users, and drive adoption until teams are working measurably better
Lift Commercial-wide AI confidence and fluency through horizontal enablement: prompt engineering best practices, Cowork setup, and capturing reusable best practices into Lantern's enterprise-wide AI Hub
Facilitate hackathons that bring full teams together to generate ideas, experiment, and build working prototypes
Establish a durable working relationship with Engineering, primarily the GTM Systems team, with an agreed way of collaborating (intake, hand-off, and build ownership) that holds across projects
Build and maintain a prioritized roadmap that sequences work effectively, delivers on commitments, and is communicated regularly to all impacted Commercial teams
What You Will Bring
6-12+ years of experience in Revenue Operations, Solutions Engineering, Software Engineering, or Product Management, with a track record of teaching yourself AI and applying it at a high-growth company
Expert with Claude Code, with hands-on experience using AI tools to build, prototype, and deploy real, shipped solutions into hosted environments, with demonstrated adoption and satisfaction from real users (not side projects)
Tool fluency across the AI and automation stack: 1-3 years experience with Clay; Microsoft Copilot; n8n, Microsoft Power Automate or similar; Databricks or similar; Salesforce; and Excel or Sheets
Strong discovery and root-cause skills: deeply curious and structured in how you understand current state and identify problem areas ripe for countermeasures
Process design instinct: you redesign the workflow so the tool fits how teams actually work, designing the tool and the process together rather than bolting one onto the other
Thrives in a high-growth, chaotic environment: tough, gritty, and able to get things done under intensity and competing priorities
Bonus if you have:
Training, enablement, and change management experience, with a personal drive to own adoption rather than hand it off (selling the solution in, documentation, office hours, re-training, and the change management around it)
An experimentation and rapid-iteration mindset: tinkers with new tools, picks things up quickly, fails fast, learns, and iterates
Previous experience in digital health point solutions or the broader healthcare industry
