6tasks in this job
76skills it uses
6tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 4 of 6 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 6 tasks
- 3Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsAnaplanMicrosoft ExcelMicrosoft Power BISAP S/4HANAMicrosoft PowerPointMicrosoft SharePoint
All 6 tasks, where people get stuck and all 76 skills
Tasks
- Build a scenario-based financial forecast
- Reconcile and substantiate balance sheet accounts at period close
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Maintain a data and AI risk register and record risk treatment and acceptance
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
Where people get stuck
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing customer creditworthiness and setting limits
- Assessing data source credibility and fitness for a question
- 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
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- Diagnosing data quality exceptions
- 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
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating responsible AI use risks
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- 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 financial statements and ratios for decisions
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- 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 entity results and eliminating intercompany balances in consolidation
- Monitoring and testing data control effectiveness
- Preserving incident evidence and timeline
- Prioritising collection actions from aged debt
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- 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 conflicting reports
- Reconciling finance subledgers and close records
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
9tasks in this job
117skills it uses
6tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 6 of 9 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 6 of 9 tasks
- 3Analysing working-capital drivers and cash conversionNeeded in 3 of 9 tasks
- 4Constructing a controlled cash-flow planNeeded in 2 of 9 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsAnaplanMicrosoft Power BIMicrosoft PowerPointMicrosoft ExcelMicrosoft SharePointSAP S/4HANA
All 9 tasks, where people get stuck and all 117 skills
Tasks
- Build a scenario-based financial forecast
- Reconcile and substantiate balance sheet accounts at period close
- Evaluate an AI use case for suitability, value and risk before adoption
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Analysing working-capital drivers and cash conversion
- Constructing a controlled cash-flow plan
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- 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
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing customer creditworthiness and setting limits
- 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
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing data quality exceptions
- 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
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- 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
- Exploring data to find patterns that survive validation
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- 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 financial statements and ratios for decisions
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- 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
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Prioritising collection actions from aged debt
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- 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 conflicting reports
- Reconciling finance subledgers and close records
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- 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
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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 Support
10tasks in this job
123skills it uses
6tools named
The skills this job leans on most
- 1Judging whether and how to use AI for a taskNeeded in 4 of 10 tasks
- 2Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 10 tasks
- 3Evaluating responsible AI use risksNeeded in 3 of 10 tasks
- 4Analysing working-capital drivers and cash conversionNeeded in 3 of 10 tasks
- 5Constructing a controlled cash-flow planNeeded in 2 of 10 tasks
ToolsAnaplanMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPointMicrosoft SharePointSAP S/4HANA
All 10 tasks, where people get stuck and all 123 skills
Tasks
- Build a scenario-based financial forecast
- Reconcile and substantiate balance sheet accounts at period close
- Evaluate an AI use case for suitability, value and risk before adoption
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set and review credit policy rules, scorecard cut-offs and segment credit limits
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Analysing working-capital drivers and cash conversion
- Constructing a controlled cash-flow plan
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- 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
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing customer creditworthiness and setting limits
- 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
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing data quality exceptions
- 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
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- 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 financial statements and ratios for decisions
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising collection actions from aged debt
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- 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 conflicting reports
- Reconciling finance subledgers and close records
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonFinancial Modelling and Decision SupportApplied AI at WorkDesigning Controlled Automations and AI-assisted Workflows
About the role
Accountable for the finance discipline's operating model and liquidity, along with close integrity, financial risk and executive reporting. Heads the finance managers and decides liquidity actions and which financial risks are accepted or escalated. Data and AI judgement sits in deciding which numbers executives can rely on and how finance adopts automation and AI.
Also called Head of FP&A