Head of HR
Accountable for the HR discipline's operating model, organisational change and people-data risk.
9tasks in this job
88skills it uses
12tools named
The skills this job leans on most
- 1Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 5 of 9 tasks
- 2Controlling disclosure risk in shared data and outputsNeeded in 5 of 9 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 5 of 9 tasks
ToolsDPOrganizerOneTrustMicrosoft ExcelMicrosoft PowerPointMicrosoft Power BIOrgVueWorkdaySympa HREcoOnlineIA-systemetDatixQualtrics
All 9 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
- Produce an accountable operating budget
- Prepare a periodic performance or risk report for a management or governance committee
- Maintain a data and AI risk register and record risk treatment and acceptance
- Model the workforce plan and organisation design options with cost and people impact
- Analyse absence patterns and recommend interventions
- Assess succession cover and talent pipeline risk for critical roles
- Analyse health and safety incidents and inspection data and track corrective actions to closure
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Maintaining a risk register with owners, triggers and responses
- Rating risks against appetite and choosing proportionate treatment
- Modelling workforce demand and supply
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Interpreting policy scope and delegated authority limits
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- 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
- 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
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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 responsible AI use risks
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- 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 the materiality of defects and deficiencies for the intended use
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- 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
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
14tasks in this job
116skills it uses
12tools named
The skills this job leans on most
- 1Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 10 of 14 tasks
- 2Controlling disclosure risk in shared data and outputsNeeded in 9 of 14 tasks
- 3Rating risks against appetite and choosing proportionate treatmentNeeded in 9 of 14 tasks
- 4Modelling workforce demand and supplyNeeded in 2 of 14 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft Power BIQualtricsDPOrganizerOneTrustMicrosoft PowerPointWorkday+ 6 more
All 14 tasks, where people get stuck and all 116 skills
Tasks
- Assess workforce movement and attrition
- 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
- Prepare a periodic performance or risk report for a management or governance committee
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Model the workforce plan and organisation design options with cost and people impact
- Analyse absence patterns and recommend interventions
- Assess succession cover and talent pipeline risk for critical roles
- Analyse health and safety incidents and inspection data and track corrective actions to closure
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- 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 source credibility and fitness for a question
- 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
- Assigning and operating data ownership and stewardship
- 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
- 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 and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- 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
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Qualtrics
- DPOrganizer
- OneTrust
- Microsoft PowerPoint
- Workday
- OrgVue
- Sympa HR
- Microsoft Excel
- EcoOnline
- IA-systemet
- Datix
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
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
12tasks in this job
120skills it uses
13tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 8 of 12 tasks
- 2Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 7 of 12 tasks
- 3Judging whether and how to use AI for a taskNeeded in 4 of 12 tasks
- 4Evaluating fitness and full cost of AI-assisted workNeeded in 4 of 12 tasks
- 5Evaluating responsible AI use risksNeeded in 4 of 12 tasks
ToolsDPOrganizerOneTrustMicrosoft PowerPointMercer compensation data and job evaluationOrgVueMicrosoft Excel+ 7 more
All 12 tasks, where people get stuck and all 120 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
- Prepare a periodic performance or risk report for a management or governance committee
- Maintain a data and AI risk register and record risk treatment and acceptance
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Benchmark jobs and set pay ranges using objective job evaluation
- Model the workforce plan and organisation design options with cost and people impact
- Analyse absence patterns and recommend interventions
- Assess succession cover and talent pipeline risk for critical roles
- Analyse health and safety incidents and inspection data and track corrective actions to closure
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- 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 source credibility and fitness for a question
- 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
- Assigning and operating data ownership and stewardship
- 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
- 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 and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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
- 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
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- 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
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- DPOrganizer
- OneTrust
- Microsoft PowerPoint
- Mercer compensation data and job evaluation
- OrgVue
- Microsoft Excel
- Workday
- Sympa HR
- Microsoft Power BI
- EcoOnline
- IA-systemet
- Datix
- Qualtrics
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
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
About the role
Accountable for the HR discipline's operating model, organisational change and people-data risk. Heads the HR managers and decides HR investment, fair selection standards and how people data and AI tools may be used. Accepts residual people risk within enterprise direction.
Also called HR Manager (when the role runs the whole HR function), Head of People