Global Senior Analyst

ResMed•Halifax, NS

3d•Onsite

About The Position

We are looking for a Global Senior Analyst to join our team 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.

Requirements

3–5+ years in analytics/WFM analytics/BI or operations strategy, ideally in global customer service or contact centers.
Advanced SQL, dashboarding, metric modeling; strong statistical reasoning.
Strong stakeholder management across multiple time zones and seniority levels.
Clear evidence of an automation-first mindset: repeatedly improved efficiency by removing manual steps, standardizing outputs, or introducing repeatable mechanisms.
Strong curiosity and comfort learning new tools quickly; consistently challenges the status quo while respecting compliance constraints.

Nice To Haves

Experience designing experiments, causal inference methods, forecasting validation, or advanced driver decomposition.
Familiarity with data platform concepts (ETL/ELT, orchestration, governance, semantic modeling, data observability).
Experience with automation approaches/tools (examples: workflow automation, low-code automation, API-based integrations, scheduled pipelines).
Practical exposure to AI/ML/GenAI in business contexts (examples: anomaly detection, forecasting enhancements, automated narratives, ticket triage, text analytics), including evaluation and governance.

Responsibilities

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.
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.
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).
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.
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.
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).
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 outcomes, or customer-impacting decisions.
Maintain clear documentation of automation logic, model assumptions, evaluation results, and decision trails.
Mentor Intraday and Data Analysts in analytical thinking, data quality, dashboarding, and storytelling.
Set analytical standards (PR/FAQ-style docs, six-pagers, WBR narratives, metric review templates).
Coach others on how to discover automation opportunities and propose AI use cases responsibly.