Roles Marketing & Communications
Communications Manager
Accountable for a communications team and for its plan, capacity and evaluation standards.
3tasks in this job
85skills it uses
7tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 1 of 3 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 1 of 3 tasks
- 3Critically evaluating marketing attribution claimsCore to this role
ToolsMeltwaterMicrosoft PowerPointMicrosoft Power BIGoogle Analytics 4QualtricsMicrosoft ExcelRetriever
All 3 tasks, where people get stuck and all 85 skills
Tasks
- Evaluate a communications or change campaign against its objectives
- Produce an accountable operating budget
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Judging whether external evidence applies to the decision context
- Mapping stakeholders' decisions, constraints and needs
- Communicating sensitive or unwelcome analytical findings
All skills
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- 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 capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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 learning transfer and performance outcomes
- 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
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- 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
- Training reproducible machine learning models
- Variance analysis
- 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
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
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- 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
6tasks in this job
104skills it uses
8tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 6 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 2 of 6 tasks
- 3Mapping stakeholders' decisions, constraints and needsNeeded in 1 of 6 tasks
- 4Communicating sensitive or unwelcome analytical findingsNeeded in 1 of 6 tasks
- 5Judging whether external evidence applies to the decision contextCore to this role
ToolsAsanaMeltwaterMicrosoft PowerPointMicrosoft Power BIGoogle Analytics 4Qualtrics+ 2 more
All 6 tasks, where people get stuck and all 104 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Produce an accountable operating budget
- Design an ethical decision-relevant experiment
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Judging whether external evidence applies to the decision context
- Communicating sensitive or unwelcome analytical findings
- Critically evaluating marketing attribution claims
All skills
- Communicating sensitive or unwelcome analytical findings
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- 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 decision-focused reports and dashboards
- Building driver-based financial forecasts
- 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
- Comparing groups
- Constructing a controlled cash-flow plan
- 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 capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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 fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- 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
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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 uncertainty changes the decision
- Making accountable decisions under uncertainty
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- 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
- 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
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- 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
- Asana
- Meltwater
- Microsoft PowerPoint
- Microsoft Power BI
- Google Analytics 4
- Qualtrics
- Microsoft Excel
- Retriever
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
104skills it uses
8tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 6 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 2 of 6 tasks
- 3Mapping stakeholders' decisions, constraints and needsNeeded in 1 of 6 tasks
- 4Communicating sensitive or unwelcome analytical findingsNeeded in 1 of 6 tasks
- 5Judging whether external evidence applies to the decision contextCore to this role
ToolsAsanaMeltwaterMicrosoft PowerPointMicrosoft Power BIGoogle Analytics 4Qualtrics+ 2 more
All 6 tasks, where people get stuck and all 104 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Produce an accountable operating budget
- Design an ethical decision-relevant experiment
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Monitor coverage, assess reputational risk and decide the communications response during an emerging issue
Where people get stuck
- Judging whether external evidence applies to the decision context
- Communicating sensitive or unwelcome analytical findings
- Critically evaluating marketing attribution claims
All skills
- Communicating sensitive or unwelcome analytical findings
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- 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 decision-focused reports and dashboards
- Building driver-based financial forecasts
- 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
- Comparing groups
- Constructing a controlled cash-flow plan
- 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 capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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 fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- 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
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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 uncertainty changes the decision
- Making accountable decisions under uncertainty
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- 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
- 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
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- 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
- Asana
- Meltwater
- Microsoft PowerPoint
- Microsoft Power BI
- Google Analytics 4
- Qualtrics
- Microsoft Excel
- Retriever
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 communications team and for its plan, capacity and evaluation standards. Allocates others' communications work and decides which campaigns take priority. Data and AI judgement sits in how campaign effectiveness is measured and in the review rules for AI-drafted content.
Also called Internal Communications Manager