Pricing Analyst
Accountable for evaluating pricing changes with statistical and commercial evidence.
3tasks in this job
6skills it uses
7tools named
The skills this job leans on most
- 1Analysing costs, allocation and margin driversNeeded in 1 of 3 tasks
- 2Analysing price realisation from list to pocket priceNeeded in 1 of 3 tasks
- 3Comparing pricing scenarios with demand and margin sensitivityNeeded in 1 of 3 tasks
ToolsMicrosoft ExcelSAP S/4HANAOracle NetSuitePythonPROS PricingMicrosoft Power BISQL (query language)
All 3 tasks, where people get stuck and all 6 skills
Tasks
- Evaluate a pricing change
- Evaluate statistical and causal evidence for an intervention's impact
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Evaluating experimental results and integrity
- Estimating and challenging causal effects
- Weighing conflicting evidence by design strength
All skills
- Analysing costs, allocation and margin drivers
- Comparing pricing scenarios with demand and margin sensitivity
- Escalating unresolved risks with evidence and clear ownership
- Identifying and quantifying decision downside and exposure
- Interpreting averages, weighted measures and mix effects
- Monitoring and testing data control effectiveness
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
6tasks in this job
85skills it uses
9tools named
The skills this job leans on most
- 1Estimating and challenging causal effectsNeeded in 3 of 6 tasks
- 2Analysing costs, allocation and margin driversNeeded in 2 of 6 tasks
- 3Analysing price realisation from list to pocket priceNeeded in 2 of 6 tasks
- 4Comparing pricing scenarios with demand and margin sensitivityNeeded in 2 of 6 tasks
- 5Evaluating experimental results and integrityNeeded in 1 of 6 tasks
ToolsMicrosoft ExcelSAP S/4HANAOracle NetSuitePythonPROS PricingMicrosoft Power BI+ 3 more
All 6 tasks, where people get stuck and all 85 skills
Tasks
- Evaluate a pricing change
- Design an ethical decision-relevant experiment
- Develop and document a fit-for-purpose analytical or ML model
- Evaluate statistical and causal evidence for an intervention's impact
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set discount guardrails and review price realisation and exceptions
Where people get stuck
All skills
- Analysing costs, allocation and margin drivers
- Analysing price realisation from list to pocket price
- Comparing pricing scenarios with demand and margin sensitivity
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Bounding and securing AI systems' tool and data access
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- 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
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- 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 ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Identifying and quantifying decision downside and exposure
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Selecting charts for evidence and decisions
- Setting and applying a release and rollback bar for models and AI systems
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
All tools
- Microsoft Excel
- SAP S/4HANA
- Oracle NetSuite
- Python
- PROS Pricing
- Microsoft Power BI
- SQL (query language)
- R (with RStudio / Posit)
- Conga CPQ (Apttus)
Paths to start
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
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
6tasks in this job
91skills it uses
9tools named
The skills this job leans on most
- 1Estimating and challenging causal effectsNeeded in 3 of 6 tasks
- 2Analysing costs, allocation and margin driversNeeded in 2 of 6 tasks
- 3Analysing price realisation from list to pocket priceNeeded in 2 of 6 tasks
- 4Comparing pricing scenarios with demand and margin sensitivityNeeded in 2 of 6 tasks
- 5Evaluating experimental results and integrityNeeded in 1 of 6 tasks
ToolsMicrosoft ExcelSAP S/4HANAOracle NetSuitePythonPROS PricingMicrosoft Power BI+ 3 more
All 6 tasks, where people get stuck and all 91 skills
Tasks
- Evaluate a pricing change
- Design an ethical decision-relevant experiment
- Develop and document a fit-for-purpose analytical or ML model
- Evaluate statistical and causal evidence for an intervention's impact
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set discount guardrails and review price realisation and exceptions
Where people get stuck
All skills
- Analysing costs, allocation and margin drivers
- Analysing price realisation from list to pocket price
- Comparing pricing scenarios with demand and margin sensitivity
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Bounding and securing AI systems' tool and data access
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- 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
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- 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 ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and applying a release and rollback bar for models and AI systems
- Setting and reviewing action thresholds for scores and model predictions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
All tools
- Microsoft Excel
- SAP S/4HANA
- Oracle NetSuite
- Python
- PROS Pricing
- Microsoft Power BI
- SQL (query language)
- R (with RStudio / Posit)
- Conga CPQ (Apttus)
Paths to start
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
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Accountable for evaluating pricing changes with statistical and commercial evidence. Models price elasticity and margin effects and tests how changes perform. Pricing rules are owned by the Pricing Lead and the Head of Commercial while the Pricing Analyst evaluates options and their effects.
Also called Revenue Management Analyst, Commercial Analyst