3tasks in this job
52skills it uses
4tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 3 tasks
- 2Judging whether uncertainty changes the decisionNeeded in 1 of 3 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 1 of 3 tasks
ToolsMicrosoft Power BISAP S/4HANASAP AribaEcoVadisWorldfavorMicrosoft ExcelCoupaMicrosoft PowerPoint
All 3 tasks, where people get stuck and all 52 skills
Tasks
- Assess supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Identifying and quantifying decision downside and exposure
- Escalating unresolved risks with evidence and clear ownership
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Analysing error patterns and failure modes in models and AI systems
- Analysing supply markets and choosing a sourcing route
- 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
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Designing and applying governed tender evaluation criteria
- Designing and comparing decision scenarios
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting supplier contract duties, terms and exposure
- Investigating procurement spend leakage and duplicate commitments
- 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
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- 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
8tasks in this job
73skills it uses
8tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 7 of 8 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 7 of 8 tasks
- 3Identifying and quantifying decision downside and exposureNeeded in 5 of 8 tasks
- 4Assessing supplier dependency, data-handling and exit riskNeeded in 4 of 8 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsMicrosoft Power BISAP S/4HANASAP AribaEcoVadisWorldfavorMicrosoft Excel+ 2 more
All 8 tasks, where people get stuck and all 73 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- 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 supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Identifying and quantifying decision downside and exposure
- Escalating unresolved risks with evidence and clear ownership
All skills
- Assessing supplier dependency, data-handling and exit risk
- Escalating unresolved risks with evidence and clear ownership
- Identifying and quantifying decision downside and exposure
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Analysing supply markets and choosing a sourcing route
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- 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 applying governed tender evaluation criteria
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Verifying benefits realisation and attribution
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportMachine Learning: from Data to Validated Models and Decisions
7tasks in this job
73skills it uses
6tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 6 of 7 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 6 of 7 tasks
- 3Identifying and quantifying decision downside and exposureNeeded in 4 of 7 tasks
- 4Assessing supplier dependency, data-handling and exit riskNeeded in 3 of 7 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsMicrosoft Power BIMicrosoft ExcelSAP AribaEcoVadisMicrosoft PowerPointCoupa
All 7 tasks, where people get stuck and all 73 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- 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 supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Identifying and quantifying decision downside and exposure
- Escalating unresolved risks with evidence and clear ownership
All skills
- Assessing supplier dependency, data-handling and exit risk
- Escalating unresolved risks with evidence and clear ownership
- Identifying and quantifying decision downside and exposure
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Analysing supply markets and choosing a sourcing route
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- 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 applying governed tender evaluation criteria
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- 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
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Verifying benefits realisation and attribution
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportMachine Learning: from Data to Validated Models and Decisions
About the role
Accountable for directing the whole procurement Function or several Heads of Procurement and for its budget and strategy. Sets procurement strategy for the organisation and holds the Heads who run its operating model to account. Data and AI judgement sits in deciding how automated buying tools are governed and how spend data shapes priorities.
Also called VP Procurement