Talent Acquisition Lead
Accountable for hiring outcomes and fairness for one business area from workforce plan to accepted offer.
2tasks in this job
32skills it uses
5tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 2 of 2 tasks
- 2Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 2 tasks
- 3Evaluating structured selection evidence fairlyNeeded in 1 of 2 tasks
- 4Applying equality law tests to people-decision criteriaNeeded in 1 of 2 tasks
ToolsTeamtailorMicrosoft Power BIMicrosoft ExcelGoogle Analytics 4SmartRecruiters
All 2 tasks, where people get stuck and all 32 skills
Tasks
- Diagnose recruitment funnel constraints
- Evaluate structured candidate evidence and recommend a fair selection
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Applying equality law tests to people-decision criteria
- Evaluating structured selection evidence fairly
- Analysing event sequences, states and durations
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Comparing groups
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing incorrect query and calculation results
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Exploring data to find patterns that survive validation
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging whether a change exceeds normal variation
- Profiling data
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising personal data and privacy duties
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonPeople and Workforce Analytics
4tasks in this job
59skills it uses
6tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 4 of 4 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 4 of 4 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 4 tasks
- 4Evaluating structured selection evidence fairlyNeeded in 1 of 4 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsTeamtailorMicrosoft Power BIGoogle Analytics 4SmartRecruitersSAP FieldglassMicrosoft Excel
All 4 tasks, where people get stuck and all 59 skills
Tasks
- Diagnose recruitment funnel constraints
- Evaluate structured candidate evidence and recommend a fair selection
- Review supplier performance against contract obligations and recommend renewal or remedy
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Evaluating structured selection evidence fairly
- Testing competing explanations with discriminating evidence
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Escalating unresolved risks with evidence and clear ownership
- Evaluating structured selection evidence fairly
- Testing competing explanations with discriminating evidence
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Applying equality law tests to people-decision criteria
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Building cohort and retention analyses with correct denominators
- 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
- Comparing groups
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Profiling data
- 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 personal data and privacy duties
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
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
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonPeople and Workforce Analytics
4tasks in this job
64skills it uses
6tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 4 of 4 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 4 of 4 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 4 tasks
- 4Evaluating structured selection evidence fairlyNeeded in 1 of 4 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsMicrosoft Power BIGoogle Analytics 4WorkdayTeamtailorSmartRecruitersSAP Fieldglass
All 4 tasks, where people get stuck and all 64 skills
Tasks
- Diagnose recruitment funnel constraints
- Evaluate structured candidate evidence and recommend a fair selection
- Review supplier performance against contract obligations and recommend renewal or remedy
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Evaluating structured selection evidence fairly
- Testing competing explanations with discriminating evidence
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Escalating unresolved risks with evidence and clear ownership
- Evaluating structured selection evidence fairly
- Testing competing explanations with discriminating evidence
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Applying equality law tests to people-decision criteria
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Building cohort and retention analyses with correct denominators
- 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
- Comparing groups
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- 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 personal data and privacy duties
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
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
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonPeople and Workforce Analytics
About the role
Accountable for hiring outcomes and fairness for one business area from workforce plan to accepted offer. Leads the area's requisitions and reviews other recruiters' work without managing them. Data and AI judgement sits in reading funnel data and adverse impact and in questioning how AI-assisted screening is used.