HR Business Partnering Manager
Accountable for a team of HR Business Partners and for the quality of people advice across their business areas.
3tasks in this job
55skills it uses
6tools named
The skills this job leans on most
- 1Modelling workforce demand and supplyNeeded in 2 of 3 tasks
- 2Analysing organisation structure, spans and critical-role coverNeeded in 2 of 3 tasks
ToolsMicrosoft Power BIQualtricsWinningtempMicrosoft PowerPointWorkdayMicrosoft Excel
All 3 tasks, where people get stuck and all 55 skills
Tasks
- Assess workforce movement and attrition
- Model the workforce plan and organisation design options with cost and people impact
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Modelling workforce demand and supply
- Analysing organisation structure, spans and critical-role cover
All skills
- Analysing organisation structure, spans and critical-role cover
- Modelling workforce demand and supply
- Analysing pay equity with raw and adjusted pay gaps
- 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 lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Controlling disclosure risk in shared data and outputs
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- 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
- Fitting, checking and interpreting inferential regression models
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting workforce metrics and populations
- 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
- Mapping personal data processing and flows
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Running sensitivity and break-even analysis on model assumptions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
4tasks in this job
56skills it uses
6tools named
The skills this job leans on most
- 1Modelling workforce demand and supplyNeeded in 2 of 4 tasks
- 2Analysing organisation structure, spans and critical-role coverNeeded in 2 of 4 tasks
ToolsMicrosoft Power BIQualtricsOrgVueMicrosoft ExcelMicrosoft SharePointWorkday
All 4 tasks, where people get stuck and all 56 skills
Tasks
- Assess workforce movement and attrition
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Model the workforce plan and organisation design options with cost and people impact
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Controlling disclosure risk in shared data and outputs
- Analysing pay equity with raw and adjusted pay gaps
- Answering individual pay transparency requests
All skills
- Analysing organisation structure, spans and critical-role cover
- Modelling workforce demand and supply
- Analysing pay equity with raw and adjusted pay gaps
- 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 lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Controlling disclosure risk in shared data and outputs
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- 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
- Fitting, checking and interpreting inferential regression models
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting workforce metrics and populations
- 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
- Mapping personal data processing and flows
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- 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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonPeople and Workforce Analytics
4tasks in this job
58skills it uses
5tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 4 of 4 tasks
- 2Analysing pay equity with raw and adjusted pay gapsNeeded in 3 of 4 tasks
- 3Modelling workforce demand and supplyNeeded in 2 of 4 tasks
- 4Analysing organisation structure, spans and critical-role coverNeeded in 2 of 4 tasks
- 5Answering individual pay transparency requestsNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMercer compensation data and job evaluationOrgVueWorkday
All 4 tasks, where people get stuck and all 58 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- Model the workforce plan and organisation design options with cost and people impact
- Assess succession cover and talent pipeline risk for critical roles
Where people get stuck
- Modelling workforce demand and supply
- Analysing organisation structure, spans and critical-role cover
All skills
- Analysing organisation structure, spans and critical-role cover
- Analysing pay equity with raw and adjusted pay gaps
- Answering individual pay transparency requests
- Controlling disclosure risk in shared data and outputs
- Modelling workforce demand and supply
- 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 lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- 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
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting workforce metrics and populations
- 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
- Mapping personal data processing and flows
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- 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
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
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 a team of HR Business Partners and for the quality of people advice across their business areas. Allocates partners' work and sets the standard of evidence-based advice. Data and AI judgement sits in deciding when people evidence is strong enough for major decisions and how sensitive findings are shared.