Explore Tasks
Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
HR and leadership receive a pay-gap analysis by comparable work with explained and unexplained gaps and disclosure-safe outputs for statutory or pay transparency reporting.
1job does it
4jobs help with it
50skills 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
- Locating and acting on personal data for individual rights requests
- Writing data quality rules
- Enforcing and auditing data access
- Applying data classification and handling rules
- Assessing privacy risk through data protection impact assessments
- Designing testable data controls
- Recognising and containing suspected personal data breaches
- Resolving consent and suppression status across systems
- Mapping personal data processing and flows
- Interpreting policy scope and delegated authority limits
- Building data protection by design into data products and pipelines
- Assessing data-driven fairness and bias
- Evaluating structured selection evidence fairly
- Applying equality law tests to people-decision criteria
- Applying research ethics and participant consent
- Evaluating jobs with an analytical, gender-neutral scheme
- Recognising and resolving ethical conflicts in data and AI work
- Finding relevant data sources
- Applying exemptions and third-party redaction to rights responses
- Answering individual pay transparency requests
- Making accountable decisions under uncertainty
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
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
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 soonPeople and Workforce Analytics