4tasks in this job
66skills it uses
9tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 2 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 3Writing verifiable requirements and acceptance criteriaNeeded in 2 of 4 tasks
- 4Evaluating product outcomes and metric integrityNeeded in 1 of 4 tasks
- 5Coordinating accountable handoffs between teamsNeeded in 1 of 4 tasks
ToolsJiraAmplitudePendoZendeskMicrosoft 365 CopilotAzure DevOpsMicrosoft ExcelGoogle Analytics 4Hotjar
All 4 tasks, where people get stuck and all 66 skills
Tasks
- Prioritise product outcomes
- Verify project deliverables and accept a controlled handoff
- Elicit, analyse and manage requirements for a data, analytics or system change
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Statistical power and sample size
All skills
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Prioritising work by evidenced value, cost, risk and confidence
- Writing verifiable requirements and acceptance criteria
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Building cohort and retention analyses with correct denominators
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- 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 and challenging customer segments
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Diagnosing digital funnel conversion from event data
- Diagnosing incorrect query and calculation results
- Diagnosing operational handoffs and flow
- Eliciting needs through interviews, workshops and observation
- 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 ML generalisation and fitness
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting project cost and schedule from progress evidence
- Formulating answerable data questions
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reconstructing a record's state at a point in time from history and audit trails
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Training reproducible machine learning models
- Verifying project progress against accepted outcomes
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
7tasks in this job
111skills it uses
11tools named
The skills this job leans on most
- 1Designing experimentsNeeded in 4 of 7 tasks
- 2Governing shared data definitionsNeeded in 2 of 7 tasks
- 3Calculating and verifying KPIs against their specificationNeeded in 2 of 7 tasks
- 4Statistical power and sample sizeNeeded in 1 of 7 tasks
- 5Evaluating experimental results and integrityNeeded in 1 of 7 tasks
ToolsAmplitudeMixpanelOneTrustGoogle Analytics 4JiraAzure DevOps+ 5 more
All 7 tasks, where people get stuck and all 111 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Prioritise product outcomes
- Define and govern a KPI metric contract
- Verify project deliverables and accept a controlled handoff
- Design an ethical decision-relevant experiment
- Evaluate statistical and causal evidence for an intervention's impact
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Evaluating experimental results and integrity
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Calculating and verifying KPIs against their specification
- Designing experiments
- Evaluating experimental results and integrity
- Governing shared data definitions
- Statistical power and sample size
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- 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
- Comparing periods consistently
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- 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 and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting needs through interviews, workshops and observation
- 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 ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- 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 project cost and schedule from progress evidence
- Formulating answerable data questions
- Identifying keys and testing referential integrity
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying project progress against accepted outcomes
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Amplitude
- Mixpanel
- OneTrust
- Google Analytics 4
- Jira
- Azure DevOps
- Microsoft Excel
- Microsoft PowerPoint
- Zendesk
- Optimizely
- Hotjar
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
7tasks in this job
116skills it uses
10tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 2 of 7 tasks
- 2Calculating and verifying KPIs against their specificationNeeded in 2 of 7 tasks
- 3Estimating and challenging causal effectsNeeded in 2 of 7 tasks
- 4Specifying BI measure semantics: additivity, aggregation and timeNeeded in 2 of 7 tasks
- 5Evaluating experimental results and integrityNeeded in 1 of 7 tasks
ToolsAmplitudeMixpanelOneTrustJiraAzure DevOpsMicrosoft Excel+ 4 more
All 7 tasks, where people get stuck and all 116 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Prioritise product outcomes
- Define and govern a KPI metric contract
- Verify project deliverables and accept a controlled handoff
- Design an ethical decision-relevant experiment
- Evaluate statistical and causal evidence for an intervention's impact
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Evaluating experimental results and integrity
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Calculating and verifying KPIs against their specification
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- 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
- Comparing periods consistently
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- 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 and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- 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 project cost and schedule from progress evidence
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Amplitude
- Mixpanel
- OneTrust
- Jira
- Azure DevOps
- Microsoft Excel
- Optimizely
- Hotjar
- Google Analytics 4
- Pendo
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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 the outcomes of a product area. Orders work by evidenced value and defines the product metrics that show progress. Accepts increments against agreed criteria and uses experiment and usage data to decide what to build next.
Also called Product Owner (where the work matches), Technical Product Manager, Product Lead