Learning & Development Specialist
Accountable for diagnosing learning needs, designing development interventions and evaluating whether they changed performance.
3tasks in this job
0skills it uses
5tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 3 of 3 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 3 tasks
- 3Evaluating learning transfer and performance outcomesNeeded in 2 of 3 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 3 tasks
ToolsEdCastWorkdayCornerstone OnDemandMicrosoft ExcelDoceboMicrosoft Power BIMicrosoft FormsMicrosoft 365 Copilot
All 3 tasks, where people get stuck and all 0 skills
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
4tasks in this job
79skills it uses
8tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 4 of 4 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 4 tasks
- 3Evaluating learning transfer and performance outcomesNeeded in 2 of 4 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 4 tasks
ToolsEdCastWorkdayCornerstone OnDemandMicrosoft ExcelDoceboMicrosoft Power BI+ 2 more
All 4 tasks, where people get stuck and all 79 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- 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
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- 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
- 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 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
- 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 responsible AI use risks
- Exercising human oversight of automated and AI outputs
- 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
- 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 whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- 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
- 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, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
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
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
4tasks in this job
85skills it uses
7tools named
The skills this job leans on most
- 1Giving specific developmental feedback on data and AI workNeeded in 4 of 4 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 4 tasks
- 3Evaluating learning transfer and performance outcomesNeeded in 2 of 4 tasks
- 4Diagnosing capability gaps from work evidence and planning developmentNeeded in 2 of 4 tasks
ToolsEdCastMicrosoft Power BIMicrosoft ExcelCornerstone OnDemandDoceboMicrosoft Forms+ 1 more
All 4 tasks, where people get stuck and all 85 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- 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
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- 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
- 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 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
- 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 responsible AI use risks
- Exercising human oversight of automated and AI outputs
- 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
- 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 uncertainty changes the decision
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- 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
- 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, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- 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
- EdCast
- Microsoft Power BI
- Microsoft Excel
- Cornerstone OnDemand
- Docebo
- Microsoft Forms
- Microsoft 365 Copilot
Paths to start
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
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 diagnosing learning needs, designing development interventions and evaluating whether they changed performance. Decides methods, formats and measures within a commissioned programme. Data and AI judgement sits in needs evidence, evaluation design and checking AI-assisted content.
Also called Talent Development Specialist, L&D Specialist, Learning Designer, Instructional Designer, Learning & Development Advisor