Learning & Development Manager
Accountable for learning priorities, the programme portfolio, its evaluation and the learning and development team.
3tasks in this job
65skills it uses
4tools named
The skills this job leans on most
- 1Evaluating learning transfer and performance outcomesNeeded in 1 of 3 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 1 of 3 tasks
ToolsWorkdayMicrosoft Power BIMicrosoft ExcelCornerstone OnDemandDoceboCoupaMicrosoft 365 Copilot
All 3 tasks, where people get stuck and all 65 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Evaluate whether workplace learning changed performance
Where people get stuck
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
All skills
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Applying research ethics and participant consent
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- 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
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Investigating procurement spend leakage and duplicate commitments
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Revising prior decisions when evidence changes
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
6tasks in this job
92skills it uses
7tools named
The skills this job leans on most
- 1Evaluating learning transfer and performance outcomesNeeded in 2 of 6 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 2 of 6 tasks
ToolsWorkdayMicrosoft Power BIMicrosoft ExcelCornerstone OnDemandDoceboCoupa+ 1 more
All 6 tasks, where people get stuck and all 92 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Evaluate whether workplace learning changed performance
- Produce an accountable operating budget
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Giving specific developmental feedback on data and AI work
- Diagnosing capability gaps from work evidence and planning development
All skills
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- 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
- Building cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- 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
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
- 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
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
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
5tasks in this job
93skills it uses
7tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 5 of 5 tasks
- 2Evaluating learning transfer and performance outcomesNeeded in 2 of 5 tasks
- 3Evaluating whether people understood, adopted and sustained a changeNeeded in 2 of 5 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 5 tasks
ToolsMicrosoft ExcelMicrosoft Power BIWorkdayCornerstone OnDemandDoceboCoupa+ 1 more
All 5 tasks, where people get stuck and all 93 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Evaluate whether workplace learning changed performance
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
All skills
- Diagnosing capability gaps from work evidence and planning development
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Giving specific developmental feedback on data and AI work
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- 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
- Building cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Designing implementable change plans
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- 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 customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- Microsoft Power BI
- Workday
- Cornerstone OnDemand
- Docebo
- Coupa
- Microsoft 365 Copilot
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
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
About the role
Accountable for learning priorities, the programme portfolio, its evaluation and the learning and development team. Decides where learning budget goes and what evidence counts as a change in performance. Data and AI judgement sits in evaluation design and in governing AI-generated learning content.
Also called L&D Manager