Support Operations Analyst
AnthropicSan Francisco, CA | New York City, NY | Seattle, WAPosted 28 February 2026
Job Description
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role:
As a Support Operations Analyst, you will build the analytical and workforce planning foundation that enables Anthropic's support organization to scale intelligently. This role sits at the intersection of data analysis, capacity planning, and operational strategy—providing the insights leadership needs to make confident decisions about staffing, investment, and service levels.
You'll own forecasting and capacity planning across our support organization, including FTE teams, AI-powered support channels, and vendor/contractor partnerships. This means building models that predict volume based on product launches, model releases, and customer growth; analyzing the relationship between support metrics and business outcomes; and ensuring we have the right resources in the right places to meet our service commitments.
This is a high-ambiguity role where you'll often be building from scratch. We're looking for someone who can create structure where none exists, ask the right questions to scope problems, and translate messy data into narratives that drive action.
Responsibilities:
Workforce Planning Forecasting
Build and maintain staffing models that translate SLA targets into headcount requirements across FTE and vendor teams
Forecast support volume by analyzing historical trends, product release calendars, model launches, and customer base growth projections
Factor AI support effectiveness (automation rates, deflection, Fin AI Agent performance) into capacity models to ensure accurate human staffing projections
Partner with vendor managers to align contractor capacity with demand forecasts and service level requirements
Model scenarios to inform strategic decisions about staffing investments, vendor mix, and coverage models
Develop frameworks for prioritizing automation initiatives based on volume impact and deflection potential
Analytics Reporting
Maintain and enhance dashboards that track productivity, response times, CSAT, queue health, and other key support metrics
Investigate the relationship between support performance and business outcomes (e.g., how response time and satisfaction impact retention and churn)
Surface trends and insights that inform operational decisions—identifying what's driving volume, where bottlenecks emerge, and where investment is needed
Translate complex data into clear recommendations for leadership and cross-functional partners
Operational Partnership
Collaborate with Support Ops, AI Support, and Human Support teams to ensure data and forecasts align with operational reality
Partner with Finance on headcount planning, budget alignment, and quarterly capacity reviews
Work with Product and Engineering to anticipate how launches and feature changes will impact support demand
Contribute to vendor performance management by establishing metrics frameworks and reporting cadences
You might be a good fit if you:
Have 4+ years of experience in workforce management, support operations analytics, business analytics, or similar roles—ideally in a support or customer success context
Are deeply analytical with strong SQL skills and experience with data warehouses (e.g., BigQuery) and analysis tools like Hex, Looker, or similar
Have hands-on experience with forecasting and capacity planning, including modeling staffing needs against service level targets
Are comfortable working with ambiguity—you can take a vague question, scope it into an answerable problem, and deliver insights that drive decisions
Understand support operations metrics (SLAs, handle time, CSAT, deflection rates) and can connect them to business impact
Have experience worki ... (truncated, view full listing at source)
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