Suki

RevOps Analyst - AI & Automation

Redwood City

Sponsorship not specified$85k-$115kDetected 11 hours ago

PythonSQLMachine LearningData VisualizationAgentic AISalesforceCRMForecastingExcelCustomer SuccessLeadershipMicrosoft Office

About the role

Our ACI doesn't just listen in the background; it functions as an invisible layer that understands, contextualizes, and helps with everything from pre-charting to revenue cycle management.
On average, clinicians who use Suki see a 60% reduction in feelings of burnout, an 81% increase in practice satisfaction, and a $1,688 increase in incremental monthly revenue per user.
Why we need you Revenue Operations at Suki runs on recurring work: leadership reporting, forecasting, quota and license management, invoicing, and the dozens of weekly and monthly rituals that keep Sales and CS aligned.

Build AI automations and agents (the core of the job)
Design, build, and operate AI agents and automations for recurring RevOps workflows, working from direction set by the team lead.
Own the app and agent layer end to end: Claude agents, deck/report generation, and no-code/API connective tissue between our tools.
Own a defined set of hands-on operational tasks - user and license management, invoicing mechanics, and reporting support.
Drive the cross-functional follow-ups that keep these moving: coordinating with Finance, Legal, Sales, and CS to close loops and unblock work.
Document what you build so it's maintainable and can scale across the team.
What you might build in your first year
A renewal pricing agent that supports renewal and expansion motions with consistent, rules-based pricing.
Our partner product, Suki for Partners, allows EHRs, telehealth platforms, RCM solutions, care management systems, and hardware partners to leverage APIs and SDKs through the Suki Developer Layer in order to deliver transformative ACI inside their own surfaces.
We are dedicated to building a company that fosters inclusion and belonging and reflects the diverse communities we serve across the country.

Around 2 years of experience in Revenue Operations, Sales/CS Ops, Business Operations, or a closely related analytical/operational role - or at least one year of hands-on AI/automation work.
Comfort with no-code/low-code automation (e.g., Zapier, n8n, Make) and connecting tools via APIs.
Working knowledge of CRM tools like Salesforce and Gainsight.

Some real coding experience (Python, SQL, or similar) - a strong plus, though not required.
Exposure to BI tools (Tableau) and modern RevOps stacks.
You see a manual, repetitive process and your instinct is to automate it.
You're happy to roll up your sleeves on the unglamorous work because you know that's where the best automation ideas come from.
You take direction well but bring creativity to how you execute.
Why work at Suki?
This isn't a replacement AI
Venture-backed: We've raised $215 M from top VCs like Venrock, First Round, Flare, and March Capital.

Prototype quickly, ship, measure, and iterate. Bias toward getting a working version live over a perfect spec.

In compliance with the State of California Pay Transparency Law, Base salary range for this role is between $85k - $115k.

This range is not inclusive of any discretionary bonus or equity package.
Help shape a better healthcare system
At Suki, we're designing healthcare technology that solves one of the biggest problems in medicine: clinician burnout.
Our proprietary Ambient Clinical Intelligence (ACI) platform is built in partnership with clinicians, with the mission of meaningfully improving their lives, their patients' lives, and the healthcare system as a whole.

Named Google's Partner of the Year for AI/ML, and recognized by Fast Company as The Next Big Thing in Tech, by Comparably for the best leadership team, and by Forbes as one of the top 50 companies in AI.
Our customers see a 9x ROI in year one - and we're just getting started.
We know we are stronger this way, and we look forward to growing our team with these shared values.

This listing is sourced directly from Suki's careers page and normalized into a canonical job model.