Senior AI Builder and Analyst, Clio

• 4+ years of experience working in a contact center or SaaS support environment
• Hands-on experience building AI-assisted workflows or tools, not just using them: prompt engineering, agentic workflows, AI QA scoring, or similar
• Strong reporting and data visualization skills (Looker, Salesforce, or advanced Excel/Sheets); comfort with SQL or a willingness to get there fast
• Working knowledge of core WFM and QA metrics (SLA, AHT, shrinkage, occupancy, utilization, DSAT, repeat contact) and what actually moves them
• A track record of translating complex, technical work into something a non-technical stakeholder can act on
• A bias for action and comfort operating without a lot of structure, this role is being built as the team's scope evolves, not stepping into something fully mapped out

Desirable

• We are currently seeking a Senior AI Builder & Analyst to support our Customer Support organization
• Customer Support, spanning Core Support, Technical Escalations, and Workforce Management, makes sure our people, tools, and processes are lined up to meet our customers where they are
• As Clio moves further into an AI-first operating model, this role is where that shift is built: the tools, workflows, and collaboration that let a lean team see further and move faster across every function in Support
• This is a builder's role first. You're not just analyzing how AI could help; you're the one building and honing the tools that make it real, then handing off something the team can run
• Designing, building, and continuously refining AI-assisted tools and workflows for Support, Technical Escalations, and Workforce Management, closing the gap between "AI could help here" and a tool the team actually uses
• Owning the technical build: prompt design, workflow logic, integration with our existing stack, and ongoing tuning as needs change
• Partnering directly with leaders across Support, TechEsc, CSOps and WFM to identify where a built tool beats a manual process, and prioritizing accordingly; identifying where AI tooling should go next
• Optimizing and maintaining the AI-assisted QA tooling that turns conversation and case data into clear signal on quality and DSAT drivers, working closely with the QA program owner
• Designing dashboards and reporting that make quality trends visible to frontline leaders, not just to Support leadership
• Building and automating reporting on core Support metrics (SLA, occupancy, utilization, shrinkage, repeat contact) wherever a built tool beats a manual process, translating the data into recommendations leadership can act on
• Partnering with People/TAcq, Finance, and Product to keep the tools you build aligned to business growth and workforce planning needs