Chief People Officer
Accountable for people outcomes across the whole enterprise as a member of the executive team.
3tasks in this job
62skills it uses
7tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 3 of 3 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 3 tasks
- 3Judging whether uncertainty changes the decisionNeeded in 1 of 3 tasks
- 4Maintaining a risk register with owners, triggers and responsesCore to this role
- 5Interpreting policy scope and delegated authority limitsCore to this role
ToolsOneTrustDPOrganizerMicrosoft PowerPointMicrosoft Power BIWorkdayMicrosoft ExcelOrgVue
All 3 tasks, where people get stuck and all 62 skills
Tasks
- Maintain a data and AI risk register and record risk treatment and acceptance
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing auditable decision models
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Writing evidence-led findings and auditable recommendations with their limits
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
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
88skills it uses
8tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 7 of 8 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 8 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 2 of 8 tasks
- 4Judging whether uncertainty changes the decisionNeeded in 1 of 8 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsDPOrganizerOneTrustMercer compensation data and job evaluationMicrosoft ExcelOrgVueMicrosoft PowerPoint+ 2 more
All 8 tasks, where people get stuck and all 88 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Evaluate an AI use case for suitability, value and risk before adoption
- Maintain a data and AI risk register and record risk treatment and acceptance
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Model and recommend the annual pay and benefits review budget and guidelines
- Model the workforce plan and organisation design options with cost and people impact
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Interpreting policy scope and delegated authority limits
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Costing a pay review budget and guideline matrix
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing auditable decision models
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- DPOrganizer
- OneTrust
- Mercer compensation data and job evaluation
- Microsoft Excel
- OrgVue
- Microsoft PowerPoint
- Microsoft Power BI
- Workday
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
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
88skills it uses
8tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 7 of 8 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 8 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 2 of 8 tasks
- 4Judging whether uncertainty changes the decisionNeeded in 1 of 8 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsDPOrganizerOneTrustMercer compensation data and job evaluationOrgVueMicrosoft ExcelMicrosoft PowerPoint+ 2 more
All 8 tasks, where people get stuck and all 88 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Evaluate an AI use case for suitability, value and risk before adoption
- Maintain a data and AI risk register and record risk treatment and acceptance
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Model and recommend the annual pay and benefits review budget and guidelines
- Model the workforce plan and organisation design options with cost and people impact
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Interpreting policy scope and delegated authority limits
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Costing a pay review budget and guideline matrix
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing auditable decision models
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- DPOrganizer
- OneTrust
- Mercer compensation data and job evaluation
- OrgVue
- Microsoft Excel
- Microsoft PowerPoint
- Microsoft Power BI
- Workday
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
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for people outcomes across the whole enterprise as a member of the executive team. Answers to the board for culture, leadership succession and workforce risk. Data and AI judgement sits in how the organisation governs people data and AI in workforce decisions.
Also called Chief HR Officer, CHRO, CPO (people)