Customer Service Team Lead
Runs a service team or queue and is accountable for its response times, case quality and first-line escalations.
3tasks in this job
11skills it uses
8tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 2 of 3 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 2 of 3 tasks
- 3Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 3 tasks
- 4Designing and appraising samplesCore to this role
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsMicrosoft ExcelWorkdayGenesys Cloud CXPuzzelZendeskFreshdeskMicrosoft Dynamics 365 Sales and Customer InsightsSalesforce
All 3 tasks, where people get stuck and all 11 skills
Tasks
- Plan service capacity, schedules and routing against service levels
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Escalating unresolved risks with evidence and clear ownership
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Judging the materiality of defects and deficiencies for the intended use
- Reading business metrics
- Reconstructing a record's state at a point in time from history and audit trails
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
3tasks in this job
32skills it uses
8tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 2 of 3 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 2 of 3 tasks
- 3Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 3 tasks
- 4Designing and appraising samplesCore to this role
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsMicrosoft ExcelWorkdayGenesys Cloud CXPuzzelZendeskFreshdesk+ 2 more
All 3 tasks, where people get stuck and all 32 skills
Tasks
- Plan service capacity, schedules and routing against service levels
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Escalating unresolved risks with evidence and clear ownership
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Analysing queue behaviour, capacity and service constraints
- Assessing data-driven fairness and bias
- Balancing territories and quotas from account potential
- Classifying AI uses against regulatory obligations
- Converting demand into capacity requirements
- Designing and comparing decision scenarios
- Designing and weighting rule-based scoring models
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Evaluating ML generalisation and fitness
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Exercising human oversight of automated and AI outputs
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Reconstructing a record's state at a point in time from history and audit trails
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonMachine Learning: from Data to Validated Models and Decisions
3tasks in this job
55skills it uses
7tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 3 of 3 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 3 of 3 tasks
- 3Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 3 tasks
- 4Designing and appraising samplesCore to this role
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsMicrosoft ExcelGenesys Cloud CXMicrosoft Power BIZendeskFreshdeskMicrosoft Dynamics 365 Sales and Customer Insights+ 1 more
All 3 tasks, where people get stuck and all 55 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Escalating unresolved risks with evidence and clear ownership
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Classifying AI uses against regulatory obligations
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and weighting rule-based scoring models
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Evaluating ML generalisation and fitness
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Exercising human oversight of automated and AI outputs
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Specifying AI transparency and explanation duties to affected people
- Specifying decision-useful reports
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- Genesys Cloud CX
- Microsoft Power BI
- Zendesk
- Freshdesk
- Microsoft Dynamics 365 Sales and Customer Insights
- Salesforce
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonMachine Learning: from Data to Validated Models and Decisions
About the role
Runs a service team or queue and is accountable for its response times, case quality and first-line escalations. Allocates cases, coaches advisors and decides when AI-suggested answers need correcting before they reach customers. Supervises the queue without owning capacity planning or service tool decisions, which sit with the Customer Service Manager.
Also called Customer Service Supervisor, Customer Service Team Leader