Global Senior Analyst

Benefits

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We are looking for a Global Senior Analyst to join our team in Poland or in Halifax. The Senior Analyst is a global mechanisms owner responsible for building scalable, standardized, and increasingly automated decision systems across Customer Service. This role leads end-to-end work across Analytics & Reporting, Central Data Platform enablement, Causal Analysis, and Planning & WFM partnership to drive measurable improvements in service performance, efficiency, cost-to-serve, and customer/employee experience. This role is intentionally technology-forward: Senior Analysts do not just "report results"they create repeatable mechanisms that detect issues early, explain KPI drivers, and trigger action across time zones, while continuously exploring new technologies (AI/ML, GenAI, automation, rules engines, workflow orchestration) to reduce manual effort and accelerate decision-making. The ideal candidate has a strong curiosity and open mindset, constantly challenges the status quo, and actively looks for ways to simplify, standardize, automate, and eliminate waste-while fully respecting legal, regulatory, and privacy requirements, and ensuring fairness, auditability, and human accountability in decisions that impact people or customers. Senior Analysts do not just "report results" they create repeatable mechanisms that detect issues early, explain drivers, and trigger action across time zones. Let's talk about Responsibilities I- Automation & Innovation Ownership (Identify Automatable Work → Define AI Use Cases → Deliver Mechanisms) Build and maintain an automation opportunity map across CS analytics and WFM-adjacent workflows (reports, dashboards, investigations, variance analysis, playbooks, documentation, ticket triage, alerting). Proactively identify high-friction, repetitive, and manual tasks that can be automated and convert into clear AI/automation use cases, including: problem statement, current process baseline, target state, expected value (hours saved, defects reduced, faster decisions), risk assessment (privacy, bias, regulatory/compliance), required inputs/data and human-in-the-loop guardrails. Partner with WFM, Ops, Product, and Tech teams to pilot, validate, and scale automation solutions (AI + rules + workflow tools), ensuring measurable outcomes and adoption. Create reusable "building blocks": certified datasets / semantic KPI layer usage patterns, templates for dashboards, narratives, driver trees, and alerts, automation playbooks (trigger → diagnosis → recommended action → tracking) Maintain an innovation backlog and mechanism roadmap; prioritize based on impact, feasibility, and risk. II- Global Mechanisms Ownership (Data → Dashboard → Alert → Playbook) Own one or more global domains (examples: Service Analytics, Benefits Analytics, Experience Survey, Workforce Productivity). Design end-to-end mechanisms that connect leading indicators (queue health, staffing gaps, backlog risk, quality signals, customer sentiment) to clear playbooks. Own one or more global domains (examples: Service Analytics, Benefits Analytics, Experience Survey, Workforce Productivity). Build mechanisms that connect leading indicators (queue health, staffing gaps, backlog risk, quality signals) to clear playbooks. Ensure adoption: dashboards are used, alerts are actionable, and actions reduce repeat incidents. Establish adoption metrics and governance (certified dashboards, definitions, owners, refresh SLAs, and change control). III- Advanced Analytics & Causal Analysis) Lead deep dives and root cause analyses to explain KPI movements (SLA, AHT, backlog, occupancy, refusal rate, transfers). Conduct Causal Analysis using experiments or quasi-experimental designs when evaluating operational changes, product launches, routing shifts, policy updates, automation/deflection initiatives, or staffing model changes Create "decision-ready" insights: tradeoffs, confidence, risks, and recommendations. IV- Forecasting / Capacity Planning Partnership Partner with Forecasting and Capacity Planning on scenario planning: seasonality, product launches, disruptions, new region onboarding. Quantify the impact of skill mix, shrinkage, staffing models, and operational policies. Translate analytical outputs into action levers that WFM/Ops can execute. V- Central Data Platform & Metric Governance Act as steward of KPI definitions, semantic layers, and core CS reporting datasets. Align data models with master data principles; ensure consistent dimensions (site, channel, skill, language, vendor, case type). Raise quality standards (reconciliation, anomaly detection, documentation) VI- Responsible AI, Risk Management, and Compliance. Ensure all automation/AI solutions respect: data privacy and confidentiality regulatory and legal requirements, internal governance and security standards, fairness and explainability (especially for people-impacting metrics). Implement human-in-the-loop controls where approvals affect payroll, performance

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