7tasks in this job
90skills it uses
7tools named
The skills this job leans on most
- 1Building driver-based financial forecastsNeeded in 5 of 7 tasks
- 2Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 5 of 7 tasks
- 3Designing and comparing decision scenariosNeeded in 4 of 7 tasks
ToolsMicrosoft Power BIAnaplanJedoxPigmentMicrosoft PowerPointSAP S/4HANAMicrosoft Excel
All 7 tasks, where people get stuck and all 90 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Forecasting business trends and turning points for planning
- Interpreting budgets and planning assumptions
- Analysing costs, allocation and margin drivers
All skills
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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 data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- 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
- 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
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- 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
- 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 business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
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
Skill Path
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
90skills it uses
8tools named
The skills this job leans on most
- 1Forecasting business trends and turning points for planningNeeded in 5 of 7 tasks
- 2Building driver-based financial forecastsNeeded in 5 of 7 tasks
- 3Designing and comparing decision scenariosNeeded in 4 of 7 tasks
- 4Interpreting budgets and planning assumptionsNeeded in 4 of 7 tasks
- 5Analysing costs, allocation and margin driversNeeded in 4 of 7 tasks
ToolsMicrosoft Power BIAnaplanOracle Hyperion and Oracle EPM Cloud (Planning, HFM, FCCS, EPBCS)PigmentSAP S/4HANAMicrosoft PowerPoint+ 2 more
All 7 tasks, where people get stuck and all 90 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing costs, allocation and margin drivers
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Forecasting business trends and turning points for planning
- Interpreting budgets and planning assumptions
- Analysing business drivers and contributions
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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 data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- 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
- 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
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- 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
- 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 operational demand at interval and site grain
- Formulating answerable data questions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Anaplan
- Oracle Hyperion and Oracle EPM Cloud (Planning, HFM, FCCS, EPBCS)
- Pigment
- SAP S/4HANA
- Microsoft PowerPoint
- Jedox
- 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
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
Skill Path
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
7tasks in this job
93skills it uses
8tools named
The skills this job leans on most
- 1Forecasting business trends and turning points for planningNeeded in 5 of 7 tasks
- 2Building driver-based financial forecastsNeeded in 5 of 7 tasks
- 3Designing and comparing decision scenariosNeeded in 4 of 7 tasks
- 4Interpreting budgets and planning assumptionsNeeded in 4 of 7 tasks
- 5Analysing costs, allocation and margin driversNeeded in 4 of 7 tasks
ToolsMicrosoft Power BIAnaplanPigmentOracle Hyperion and Oracle EPM Cloud (Planning, HFM, FCCS, EPBCS)Microsoft ExcelSAP S/4HANA+ 2 more
All 7 tasks, where people get stuck and all 93 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Running sensitivity and break-even analysis on model assumptions
All skills
- Analysing costs, allocation and margin drivers
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Forecasting business trends and turning points for planning
- Interpreting budgets and planning assumptions
- Analysing business drivers and contributions
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- 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 data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- 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
- 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
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- 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
- 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 operational demand at interval and site grain
- Formulating answerable data questions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Anaplan
- Pigment
- Oracle Hyperion and Oracle EPM Cloud (Planning, HFM, FCCS, EPBCS)
- Microsoft Excel
- SAP S/4HANA
- Microsoft PowerPoint
- Jedox
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
Skill Path
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
About the role
Accountable for running the planning and forecasting cycle, its quality and the FP&A team. Decides the planning calendar, how assumptions are governed and which forecast risks reach the Head of Finance. Data and AI judgement sits in deciding which models and tools the forecast can rely on.