4tasks in this job
52skills it uses
10tools named
The skills this job leans on most
- 1Estimating and challenging causal effectsNeeded in 2 of 4 tasks
- 2Diagnosing digital funnel conversion from event dataNeeded in 1 of 4 tasks
- 3Building cohort and retention analyses with correct denominatorsNeeded in 1 of 4 tasks
- 4Evaluating experimental results and integrityCore to this role
- 5Evaluating product outcomes and metric integrityCore to this role
ToolsGoogle Analytics 4AmplitudeAdobe AnalyticsSQL (query language)Google BigQuerySnowflakeDatabricksMicrosoft ExcelMixpanelPendo
All 4 tasks, where people get stuck and all 52 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Answer a commissioned business question with reproducible query-based analysis
- Evaluate statistical and causal evidence for an intervention's impact
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Designing and validating digital analytics event measurement
- Statistical power and sample size
- Designing experiments
All skills
- Building cohort and retention analyses with correct denominators
- Diagnosing digital funnel conversion from event data
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing periods consistently
- Constructing and challenging customer segments
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting trends and seasonality
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Mapping customer journeys and evidence gaps
- Measuring campaign outcomes with valid denominators
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising misleading numerical claims
- Reconstructing a record's state at a point in time from history and audit trails
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Validating records by matching, recomputation and end-to-end tracing
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
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
8tasks in this job
111skills it uses
16tools named
The skills this job leans on most
- 1Designing experimentsNeeded in 5 of 8 tasks
- 2Designing and validating digital analytics event measurementNeeded in 2 of 8 tasks
- 3Statistical power and sample sizeNeeded in 2 of 8 tasks
- 4Evaluating experimental results and integrityNeeded in 2 of 8 tasks
- 5Estimating and challenging causal effectsNeeded in 2 of 8 tasks
ToolsGoogle Tag ManagerTwilio SegmentAmplitudeMixpanelLookerPendo+ 10 more
All 8 tasks, where people get stuck and all 111 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Prioritise product outcomes
- Investigate customer-experience evidence
- Design an ethical decision-relevant experiment
- Deliver and sustain a trusted BI report or dashboard
- Design and build a tested, governed analytical data model
- Evaluate statistical and causal evidence for an intervention's impact
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Evaluating experimental results and integrity
- Evaluating product outcomes and metric integrity
- Weighing conflicting evidence by design strength
All skills
- Designing and validating digital analytics event measurement
- Designing experiments
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Statistical power and sample size
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- 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
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing periods consistently
- Constructing and challenging customer segments
- 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 versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing dimensional models with explicit grain
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Engineering production data transformations
- 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 whether people understood, adopted and sustained a change
- 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
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- 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 uncertainty changes the decision
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Google Tag Manager
- Twilio Segment
- Amplitude
- Mixpanel
- Looker
- Pendo
- Google BigQuery
- Optimizely
- Python
- Tableau
- Microsoft Power BI
- Qlik Sense
- dbt
- SQL (query language)
- Google Analytics 4
- Snowflake
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
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
8tasks in this job
121skills it uses
17tools named
The skills this job leans on most
- 1Designing experimentsNeeded in 5 of 8 tasks
- 2Statistical power and sample sizeNeeded in 2 of 8 tasks
- 3Evaluating experimental results and integrityNeeded in 2 of 8 tasks
- 4Estimating and challenging causal effectsNeeded in 2 of 8 tasks
- 5Diagnosing digital funnel conversion from event dataNeeded in 1 of 8 tasks
ToolsGoogle Tag ManagerTwilio SegmentGoogle Analytics 4AmplitudeMicrosoft Exceldbt+ 11 more
All 8 tasks, where people get stuck and all 121 skills
Tasks
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Prioritise product outcomes
- Define and govern a KPI metric contract
- Investigate customer-experience evidence
- Design an ethical decision-relevant experiment
- Deliver and sustain a trusted BI report or dashboard
- Evaluate statistical and causal evidence for an intervention's impact
- Diagnose product adoption, funnel and retention behaviour
Where people get stuck
- Evaluating experimental results and integrity
- Evaluating product outcomes and metric integrity
- Weighing conflicting evidence by design strength
All skills
- Designing experiments
- Diagnosing digital funnel conversion from event data
- Estimating and challenging causal effects
- Evaluating experimental results and integrity
- Statistical power and sample size
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- 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
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing periods consistently
- Constructing and challenging customer segments
- 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 validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing dimensional models with explicit grain
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Engineering production data transformations
- 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 whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- 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
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- 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 uncertainty changes the decision
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- 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
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
All tools
- Google Tag Manager
- Twilio Segment
- Google Analytics 4
- Amplitude
- Microsoft Excel
- dbt
- Mixpanel
- Pendo
- Google BigQuery
- Python
- Optimizely
- Looker
- Tableau
- Microsoft Power BI
- Qlik Sense
- Snowflake
- Databricks
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
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
About the role
Accountable for the evidence behind product decisions: how people use the product, what experiments show and how certain those results are. Builds funnel, retention and feature usage analyses and evaluates A/B tests from product event data. The Product Manager decides priority while the Product Analyst supplies the evidence and states its limits.