Learning & Development Lead
Accountable for a learning programme or an organisation's L&D plan from needs analysis to evaluation, including its design and impact evidence.
2tasks in this job
8skills it uses
4tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 2 of 2 tasks
- 2Evaluating learning transfer and performance outcomesNeeded in 1 of 2 tasks
ToolsCornerstone OnDemandWorkdayMicrosoft ExcelDoceboCoupaMicrosoft FormsMicrosoft 365 Copilot
All 2 tasks, where people get stuck and all 8 skills
Tasks
- Evaluate whether workplace learning changed performance
- Design, administer and quality-check a professional survey
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
- Choosing an analytical method to fit the question and data
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Interpreting trends and seasonality
- Measuring campaign outcomes with valid denominators
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
Paths to start
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
6tasks in this job
105skills it uses
7tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 6 of 6 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 6 tasks
- 3Evaluating learning transfer and performance outcomesNeeded in 2 of 6 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 6 tasks
ToolsCornerstone OnDemandWorkdayMicrosoft ExcelDoceboCoupaMicrosoft Forms+ 1 more
All 6 tasks, where people get stuck and all 105 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
- Evaluate whether workplace learning changed performance
- Produce an accountable operating budget
- Design, administer and quality-check a professional survey
- 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 event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing incorrect query and calculation results
- 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
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- 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 customer feedback and qualitative evidence
- 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 external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- 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
- Modelling workforce demand and supply
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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
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
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
6tasks in this job
106skills it uses
8tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 6 of 6 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 6 tasks
- 3Evaluating learning transfer and performance outcomesNeeded in 2 of 6 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 6 tasks
ToolsMicrosoft ExcelWorkdayCornerstone OnDemandDoceboCoupaTotara+ 2 more
All 6 tasks, where people get stuck and all 106 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
- Design, administer and quality-check a professional survey
- 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 event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing incorrect query and calculation results
- 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
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- 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 customer feedback and qualitative evidence
- 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 external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- 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
- Modelling workforce demand and supply
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- 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
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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
All tools
- Microsoft Excel
- Workday
- Cornerstone OnDemand
- Docebo
- Coupa
- Totara
- Microsoft Forms
- 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
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
About the role
Accountable for a learning programme or an organisation's L&D plan from needs analysis to evaluation, including its design and impact evidence. Leads the work and reviews specialists and providers without line management. Data and AI judgement sits in evaluation design and in checking AI-generated learning content.
Also called L&D Lead