Talent Acquisition Manager
Accountable for a recruiting team and for how fairly and efficiently the organisation fills roles.
3tasks in this job
67skills it uses
5tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 3 of 3 tasks
- 2Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 3 tasks
- 3Evaluating structured selection evidence fairlyNeeded in 1 of 3 tasks
- 4Applying equality law tests to people-decision criteriaNeeded in 1 of 3 tasks
ToolsTeamtailorSmartRecruitersMicrosoft Power BISAP FieldglassMicrosoft Excel
All 3 tasks, where people get stuck and all 67 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
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
- Testing competing explanations with discriminating evidence
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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
- Comparing options against explicit decision criteria
- 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
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- 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 policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- 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
- 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
- Writing verifiable requirements and acceptance criteria
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
5tasks in this job
90skills it uses
6tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 5 of 5 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 5 of 5 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 5 tasks
- 4Evaluating structured selection evidence fairlyNeeded in 1 of 5 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsTeamtailorSmartRecruitersMicrosoft Power BIWorkdaySAP FieldglassMicrosoft Excel
All 5 tasks, where people get stuck and all 90 skills
Tasks
- Diagnose recruitment funnel constraints
- Evaluate an AI use case for suitability, value and risk before adoption
- 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 data classification and handling rules
- 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 data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Comparing options against explicit decision criteria
- 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 AI-assisted workflows with bounded tools and human authority
- Designing and interpreting hypothesis tests
- Designing and running evaluations of AI systems
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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 policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Protecting sensitive data in AI-assisted work
- 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 containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- 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 data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
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
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
6tasks in this job
91skills it uses
5tools named
The skills this job leans on most
- 1Testing competing explanations with discriminating evidenceNeeded in 6 of 6 tasks
- 2Escalating unresolved risks with evidence and clear ownershipNeeded in 6 of 6 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 1 of 6 tasks
- 4Evaluating structured selection evidence fairlyNeeded in 1 of 6 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsMicrosoft Power BITeamtailorOneTrustSmartRecruitersSAP Fieldglass
All 6 tasks, where people get stuck and all 91 skills
Tasks
- Diagnose recruitment funnel constraints
- Evaluate an AI use case for suitability, value and risk before adoption
- Evaluate structured candidate evidence and recommend a fair selection
- Review supplier performance against contract obligations and recommend renewal or remedy
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- 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 data classification and handling rules
- 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 data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Comparing options against explicit decision criteria
- 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 AI-assisted workflows with bounded tools and human authority
- Designing and interpreting hypothesis tests
- Designing and running evaluations of AI systems
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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 policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Protecting sensitive data in AI-assisted work
- 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 containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- 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 data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
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
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
About the role
Accountable for a recruiting team and for how fairly and efficiently the organisation fills roles. Decides team priorities, capacity across requisitions, selection standards and which sourcing channels and tools to use. Data judgement sits in reading funnel performance and adverse impact correctly and in governing AI-assisted screening.
Also called Recruitment Manager