3tasks in this job
40skills it uses
5tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 3 of 3 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 3 tasks
- 3Evaluating product outcomes and metric integrityNeeded in 2 of 3 tasks
- 4Verifying benefits realisation and attributionNeeded in 2 of 3 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 3 tasks
ToolsMicrosoft Power BIAmplitudeQualtricsAzure OpenAI Service / Azure AI FoundryMicrosoft ExcelMicrosoft PowerPointJira
All 3 tasks, where people get stuck and all 40 skills
Tasks
- Prioritise product outcomes
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Eliciting the decision behind an analytical request
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Revising prior decisions when evidence changes
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
6tasks in this job
92skills it uses
7tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 3 of 6 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 6 tasks
- 3Evaluating product outcomes and metric integrityNeeded in 2 of 6 tasks
- 4Verifying benefits realisation and attributionNeeded in 2 of 6 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
ToolsMicrosoft Power BIAmplitudeQualtricsAzure OpenAI Service / Azure AI FoundryMicrosoft ExcelMicrosoft PowerPoint+ 1 more
All 6 tasks, where people get stuck and all 92 skills
Tasks
- Prioritise product outcomes
- Define and govern a KPI metric contract
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- 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
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- 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 semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing operational handoffs and flow
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating fitness and full cost of AI-assisted work
- 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
- Forecasting business trends and turning points for planning
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- 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
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Writing data quality rules
- 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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
6tasks in this job
94skills it uses
7tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 3 of 6 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 6 tasks
- 3Evaluating product outcomes and metric integrityNeeded in 2 of 6 tasks
- 4Verifying benefits realisation and attributionNeeded in 2 of 6 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
ToolsAmplitudeJiraQualtricsMicrosoft Power BIAzure OpenAI Service / Azure AI FoundryMicrosoft PowerPoint+ 1 more
All 6 tasks, where people get stuck and all 94 skills
Tasks
- Prioritise product outcomes
- Define and govern a KPI metric contract
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- 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
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- 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 semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing operational handoffs and flow
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating fitness and full cost of AI-assisted work
- 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
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- 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
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Amplitude
- Jira
- Qualtrics
- Microsoft Power BI
- Azure OpenAI Service / Azure AI Foundry
- Microsoft PowerPoint
- Microsoft Excel
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
About the role
Accountable for the product portfolio and how the product operating model works. Decides which products and bets are funded and what outcomes they must show. Manages group product managers and sets the evidence and metric standards that product teams use in their decisions.
Also called Chief Product Owner, Head of Data Products, Head of AI Product