Explore Tasks
Diagnose recruitment funnel constraints
Talent acquisition leadership receives stage conversion and time-to-fill diagnosis by role family and source with the binding constraint named.
2jobs do it
1job helps with it
55skills it needs
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Fitting, checking and interpreting inferential regression models
- Reading data tables: rows, fields, types and units
- Interpreting averages, weighted measures and mix effects
- Interpreting missing and unavailable data
- Judging whether a change exceeds normal variation
- Describing distributions with summaries and plots
- Comparing groups
- Interpreting correlation without inferring cause
- Writing evidence-led findings and auditable recommendations with their limits
- Recognising personal data and privacy duties
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Analysing staged funnels: conversion, time in stage and slippage
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Analysing pay equity with raw and adjusted pay gaps
- Building cohort and retention analyses with correct denominators
- Writing maintainable code for analytical and data work
- Estimating quantities and differences with intervals
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Calculating and interpreting percentages, rates and ratios
- Formulating answerable data questions
- Assessing data source credibility and fitness for a question
- Profiling data
- Standardising and cleaning data under agreed rules
- Preparing analysis-ready datasets reproducibly
- Choosing and justifying missing-data treatments
- Exploring data to find patterns that survive validation
- Investigating anomalies
- Designing and interpreting hypothesis tests
- Reading charts and dashboards correctly, including their state
- Selecting charts for evidence and decisions
- Defining the analysis population: filters, nulls, dates and time zones
- Analysing event sequences, states and durations
- Diagnosing incorrect query and calculation results
- Decomposing one's own tasks for AI assistance
- Training reproducible machine learning models
- Eliciting the decision behind an analytical request
- Designing efficient and reproducible investigations
- Choosing an analytical method to fit the question and data
- Making accountable decisions under uncertainty
- Interpreting policy scope and delegated authority limits
Paths that teach the skills it needs
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
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
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
Coming soonFraming Questions and Sourcing Evidence for Investigations