Senior Workforce Management Specialist

The Senior Workforce Management Specialist owns workforce planning and capacity management for CentralSquare's customer support organization during a period of significant AI-driven change. As AI deflection scales and the mix of cases reaching human agents shifts toward higher complexity, this role ensures staffing models, scheduling, and headcount projections stay ahead of the curve rather than lagging behind it.

This role sits within the AI Innovation Lab and reports to the Director, CX Operations. It works in close partnership with Support leadership to connect WFM decisions directly to AI program outcomes. Success means the support organization is always staffed to the right level for the right work, with data-backed recommendations that leadership can act on.

Capacity Planning & Forecasting

•       Own capacity planning for the support organization: build and maintain forecasting models that account for ticket volume, AI deflection rates, handle time trends, and case complexity shifts

•       Develop staffing models that reflect both current state and projected AI adoption milestones; update models as deflection rate changes

•       Provide headcount recommendations to Support leadership and the Director, CX Operations with clear data backing and scenario analysis

•       Maintain rolling 30/60/90-day capacity forecasts; flag risks proactively before they become staffing gaps or overages

Scheduling & Utilization

•       Own scheduling for the support organization: balance coverage requirements against agent availability, SLA targets, and cost efficiency

•       Identify utilization gaps and overstaffing patterns; make recommendations to optimize agent time across channels and shifts

•       Partner with Support managers to understand seasonal patterns, product release impacts, and other volume drivers that affect scheduling

•       Track and report on schedule adherence, utilization rates, and SLA performance against staffing models

AI Integration

•       Maintain a real-time view of how AI deflection is affecting workload by channel, product, and case type; ensure WFM models are updated to reflect actual AI impact rather than historical averages

•       Work closely with the Director, CX Operations to understand the AI development pipeline and build staffing scenarios that account for upcoming automation milestones

•       Flag cases where AI deflection improvements are creating staffing imbalances (e.g., overstaffing on deflected case types, understaffing on escalated complex cases)

•       Contribute WFM data to the support AI analytics scorecard: deflection-adjusted headcount, cost per handled case, and utilization trends

Reporting & Analytics

•       Own WFM reporting: produce regular capacity, utilization, and forecast accuracy reports for Support leadership and the Director, CX Operations

•       Maintain a WFM dashboard that gives real-time visibility into staffing vs. volume across all channels and product lines

•       Track and analyze handle time trends by case type to identify where AI assist tools are working and where agent efficiency opportunities remain

•       Provide data inputs for headcount budget planning cycles

Process & Tools

•       Own the WFM tooling stack; evaluate and recommend improvements as the support organization evolves

•       Document WFM processes and maintain standard operating procedures for forecasting, scheduling, and capacity review cycles

•       Partner with Support Operations on intraday management and real-time adjustments

Education & Certification

•       B.A. or B.S. degree required

•       CWPP, IEX, or comparable WFM certification preferred

Experience

•       3+ years of workforce management experience in a support, contact center, or customer operations environment

•       Demonstrated experience building capacity forecasts and staffing models from scratch, not just maintaining inherited models

•       Experience in an environment undergoing AI or automation adoption where WFM models had to be recalibrated; strongly preferred

•       Proficiency with WFM tooling (e.g., Verint, NICE, Calabrio, Assembled, Playvs, or comparable); required

•       SaaS, PE-backed technology, or government/public sector software experience preferred

Skills

•       Strong quantitative and analytical skills: able to build, stress-test, and communicate staffing models to both operational and executive audiences

•       Ability to translate AI program roadmap data into WFM scenarios; comfort working at the intersection of operations and technology

•       Clear written and verbal communication; able to present headcount recommendations with data backing to Support leadership and VP-level stakeholders

•       Detail orientation and process rigor: WFM errors have direct operational consequences

•       Proficiency with Excel or Google Sheets for model building; familiarity with BI tools (Tableau, Looker, or equivalent) preferred

Preferred

•       Experience integrating WFM models with AI deflection data from platforms like Forethought, Intercom Fin, or Salesforce Agentforce

•       Familiarity with Salesforce reporting for case volume and handle time data

•       Experience in public safety, public administration, or mission-critical government software

In the first 90 days:

•       Complete a full audit of existing WFM models, scheduling practices, and forecast accuracy; document gaps

•       Establish a baseline that connects current staffing levels to AI deflection data by channel and product

•       Deliver a first-version 90-day capacity forecast reviewed and validated by Support leadership

•       Understand the AI development pipeline well enough to build preliminary staffing scenarios for upcoming automation milestones

At six months:

•       Have WFM models updated to reflect AI-deflected volume; headcount recommendations grounded in real deflection data rather than historical averages

•       Have a regular forecast-to-actual review cadence running with Support leadership

•       Have identified and acted on at least one scheduling optimization that improves utilization or reduces cost without impacting SLA

•       Be contributing WFM data to the unified support AI scorecard