Roles Marketing & Communications
Communications Officer
Accountable for drafting, publishing and reporting on internal and external communications to an agreed plan.
2tasks in this job
0skills it uses
6tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 1 of 2 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 1 of 2 tasks
- 3Critically evaluating marketing attribution claimsCore to this role
ToolsMeltwaterSprout SocialGoogle Analytics 4Microsoft ExcelMicrosoft SharePointNetigateSurveyMonkeyRetriever
All 2 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
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
3tasks in this job
64skills it uses
8tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 2 of 3 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 1 of 3 tasks
- 3Critically evaluating marketing attribution claimsCore to this role
ToolsMeltwaterSprout SocialGoogle Analytics 4Microsoft ExcelMicrosoft SharePointNetigate+ 2 more
All 3 tasks, where people get stuck and all 64 skills
Tasks
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- 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 event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- 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
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- 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 interpreting hypothesis tests
- Designing and quality-checking survey instruments
- 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 incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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 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
- Investigating anomalies
- 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, 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
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
4tasks in this job
65skills it uses
8tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 2 of 4 tasks
- 2Communicating sensitive or unwelcome analytical findingsNeeded in 2 of 4 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 1 of 4 tasks
- 4Judging whether external evidence applies to the decision contextCore to this role
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMeltwaterSprout SocialGoogle Analytics 4Microsoft ExcelMicrosoft SharePointNetigate+ 2 more
All 4 tasks, where people get stuck and all 65 skills
Tasks
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- 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 event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- 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
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- 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 interpreting hypothesis tests
- Designing and quality-checking survey instruments
- 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 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 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
- Investigating anomalies
- 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, 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
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 drafting, publishing and reporting on internal and external communications to an agreed plan. Produces news items, intranet and social content and channel reports without managing people. Data and AI judgement sits in reporting reach and engagement honestly and in checking AI-drafted content for accuracy and tone.
Also called Communications Executive, Communications Assistant, Internal Communications Officer, Communications Specialist, PR Executive