4tasks in this job
64skills it uses
6tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 4 tasks
- 2Judging whether uncertainty changes the decisionNeeded in 1 of 4 tasks
- 3Interpreting sustainability reporting standards and metric boundariesNeeded in 1 of 4 tasks
- 4Testing the completeness and accuracy of reports and data that controls rely onCore to this role
- 5Interpreting policy scope and delegated authority limitsCore to this role
ToolsAnaplanMicrosoft ExcelMicrosoft Power BIMicrosoft SharePointWorkivaPosition GreenMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 64 skills
Tasks
- Model cash needs and defend a liquidity action
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assure the data behind statutory and sustainability disclosures before sign-off
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Constructing a controlled cash-flow plan
- Analysing working-capital drivers and cash conversion
- Testing investment business cases under uncertainty
All skills
- Interpreting sustainability reporting standards and metric boundaries
- Presenting and defending evidence-based recommendations to decision-makers
- 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-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
- Assessing the control and reliability of end-user calculation files
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- Designing auditable decision models
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- 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 correlation without inferring cause
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Making accountable decisions under uncertainty
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
77skills it uses
7tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 5 of 7 tasks
- 2Testing investment business cases under uncertaintyNeeded in 5 of 7 tasks
- 3Constructing a controlled cash-flow planNeeded in 3 of 7 tasks
- 4Analysing working-capital drivers and cash conversionNeeded in 3 of 7 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsAnaplanMicrosoft ExcelMicrosoft Power BIMicrosoft SharePointWorkivaPosition Green+ 1 more
All 7 tasks, where people get stuck and all 77 skills
Tasks
- Build a scenario-based financial forecast
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Model cash needs and defend a liquidity action
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assure the data behind statutory and sustainability disclosures before sign-off
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Maintaining a risk register with owners, triggers and responses
- Rating risks against appetite and choosing proportionate treatment
All skills
- Constructing a controlled cash-flow plan
- Presenting and defending evidence-based recommendations to decision-makers
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assessing the control and reliability of end-user calculation files
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- 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 correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
8tasks in this job
86skills it uses
8tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 6 of 8 tasks
- 2Constructing a controlled cash-flow planNeeded in 3 of 8 tasks
- 3Analysing working-capital drivers and cash conversionNeeded in 3 of 8 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 8 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsAnaplanMicrosoft PowerPointMicrosoft Power BIMicrosoft ExcelMicrosoft 365 CopilotMicrosoft SharePoint+ 2 more
All 8 tasks, where people get stuck and all 86 skills
Tasks
- Build a scenario-based financial forecast
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Model cash needs and defend a liquidity action
- Design and govern the operating model for data, analytics and AI work in a function
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assure the data behind statutory and sustainability disclosures before sign-off
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
All skills
- Constructing a controlled cash-flow plan
- Maintaining a risk register with owners, triggers and responses
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing enforceable data governance policies
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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 correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Anaplan
- Microsoft PowerPoint
- Microsoft Power BI
- Microsoft Excel
- Microsoft 365 Copilot
- Microsoft SharePoint
- Workiva
- Position Green
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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportDelivering Data and AI Projects with Evidence
About the role
Accountable for finance across the whole enterprise and for its capital, liquidity and financial risk. Sits on the executive team and answers to the board for enterprise financial direction. Data and AI judgement sits in how the enterprise governs financial data, automation and AI.
Also called CFO