Job description At Arctic Wolf, we're not just navigating the cybersecurity landscape - we're redefining it. Our global team of dedicated Pack members is driving innovation and setting new industry standards every day. Our impact speaks for itself: we've earned recognition on the

Forbes Cloud 100, CNBC Disruptor 50, Fortune Future 50, and Fortune Cyber 60

lists, and we recently took home the

2024 CRN Products of the Year

award. We’re proud to be named a

Leader

in the

IDC MarketScape

for Worldwide Managed Detection and Response Services and earning a

Customers' Choice

distinction from

Gartner Peer Insights . Our

Aurora Platform

also received

CRN’s Products of the Year

award in the inaugural

Security Operations Platform

category. Join a company that’s not only leading, but also shaping, the future of security operations. Our mission is straightforward: End Cyber Risk. We’re looking for a Principal, S2 Workforce Strategy and Intelligence Lead to be part of making that happen. About the Role:

The Principal Workforce & Productivity Analytics Lead serves as the system of record for workforce productivity, operational efficiency, and value realization across Arctic Wolf's Security Services organization (S2). This role is responsible for establishing a comprehensive workforce intelligence framework that quantifies how work flows through the organization, where effort is being spent, what value is being generated, and how investments in automation, AI, process improvement, and product capabilities translate into measurable business outcomes. The successful candidate will provide executive leadership with a transparent, defensible view of workforce productivity, cost-to-serve, operational efficiency,



and return on investment, enabling fact-based decisions on staffing, automation strategy, product priorities, and organizational scale. Primary Responsibilities:

Workforce Productivity and Intelligence Measurement

Develop and maintain a comprehensive workforce intelligence model that quantifies: Work entering the organization Work exiting the organization Capacity created through automation and process improvements Capacity consumed by new products, services, customer growth, and operational requirements Productivity trends across teams, functions, shifts, and workflows Create a defensible productivity accounting framework that distinguishes between: Gross savings Net savings Capacity creation Capacity redeployment Demand growth Non-value-added work Operational inefficiencies Savings Realization Management

Establish and maintain a benefits realization framework that tracks every approved productivity initiative from projected benefit through realized outcome. Measure and report: Expected savings In-flight savings Realized savings Avoided costs Capacity redeployment Variance to business case Identify and explain gaps between projected and realized outcomes, including demand increases, adoption challenges, workflow inefficiencies, quality impacts, and incomplete process adoption. Operational Efficiency Analysis





Identify sources of productivity leakage across the S2 organization, including: Process variation Duplicate effort Rework Escalation loops Tool switching Manual data collection Low-value administrative work Excessive investigation effort Poor technology adoption Build evidence-based recommendations to improve workforce efficiency and increase value- generating activity. Partner with Product Management, Data Science, and Engineering teams to prioritize roadmap investments by quantifying operational friction, workforce impact, customer impact, and potential business value. Create measurable feedback loops to verify that released capabilities deliver the outcomes and productivity gains included in the original business case. Key Skills:

Workforce Economics

Productivity modeling Cost-to-serve analysis Benefits realization Capacity planning Forecasting Analytics

SQL Power BI Tableau Snowflake Python R preferred AI Analytics

Process mining Task mining ML forecasting Agent performance analytics Human plus AI productivity measurement Minimum Qualifications

8+ years in workforce management analytics, capacity planning, or task-mix and time-in-motion analysis within a 24x7 operational environment, including direct, prior ownership of this exact function. Demonstrated experience partnering directly with Finance and Product organizations to connect operational data to cost and roadmap decisions. Hands-on experience applying AI and machine learning within an analytics practice, such as modeling task complexity, forecasting capacity, or evaluating automation ROI, not just familiarity with AI tools. Advanced SQL and BI/analytics tooling fluency, with a track record of building executive-facing dashboa

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