Skills Data Science Advanced Candidate Data Analysis Workflow

Advanced Candidate Data Analysis Workflow

v20260423
juicebox-core-workflow-b
This advanced workflow enables users to perform deep analyses on large-scale people intelligence datasets. Users can build custom queries with multi-dimensional filters, run comparisons across different candidate pools (e.g., Q1 vs Q4), and aggregate skill density by geographical region. It is essential for talent acquisition teams needing to identify market trends and optimize hiring strategies beyond standard search functions.
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Overview

Juicebox — Advanced Analysis

Overview

Build custom queries, apply multi-dimensional filters, and run cross-dataset analysis
on your Juicebox people-intelligence data. Use this workflow when you need to go beyond
standard search — comparing candidate pools across roles, analyzing skill density by
geography, or identifying talent trends over time. This is the secondary workflow;
for basic search and enrichment, see juicebox-core-workflow-a.

Instructions

Step 1: Build a Custom Query with Filters

const query = await client.analysis.query({
  dataset: 'candidates',
  filters: [
    { field: 'skills', operator: 'contains_any', value: ['TypeScript', 'Rust', 'Go'] },
    { field: 'experience_years', operator: 'gte', value: 5 },
    { field: 'location.country', operator: 'eq', value: 'US' },
  ],
  sort: { field: 'relevance_score', order: 'desc' },
  limit: 100,
});
console.log(`Found ${query.total} candidates matching filters`);
query.results.forEach(c =>
  console.log(`  ${c.name} — ${c.title} (${c.relevance_score}/100)`)
);

Step 2: Run Cross-Dataset Comparison

const comparison = await client.analysis.compare({
  datasets: ['candidates_q1_2026', 'candidates_q4_2025'],
  group_by: 'primary_skill',
  metrics: ['count', 'avg_experience', 'avg_salary_estimate'],
});
comparison.groups.forEach(g =>
  console.log(`${g.skill}: Q1=${g.datasets[0].count} vs Q4=${g.datasets[1].count} (${g.delta > 0 ? '+' : ''}${g.delta}%)`)
);

Step 3: Aggregate Skill Density by Region

const density = await client.analysis.aggregate({
  dataset: 'candidates',
  group_by: 'location.metro_area',
  metric: 'skill_density',
  skill_filter: ['ML Engineering', 'Data Science'],
  top_n: 10,
});
density.regions.forEach(r =>
  console.log(`${r.metro}: ${r.candidate_count} candidates, density=${r.density_score}`)
);

Step 4: Export Analysis Results

const exportJob = await client.analysis.export({
  query_id: query.id,
  format: 'csv',
  fields: ['name', 'email', 'primary_skill', 'experience_years', 'location'],
});
console.log(`Export ready: ${exportJob.download_url} (${exportJob.row_count} rows)`);

Error Handling

IssueCauseFix
400 Invalid filterUnsupported operator for field typeCheck field schema with client.schema.fields()
404 Dataset not foundStale dataset ID or typoList datasets with client.datasets.list()
408 Query timeoutToo many filters on large datasetAdd limit or narrow date range
429 Rate limitedExceeded analysis quotaImplement backoff; check plan limits
Partial comparison dataOne dataset has sparse coverageExpected — use include_nulls: true for completeness

Output

A successful workflow produces filtered candidate lists with relevance scores,
cross-dataset comparison tables showing talent market shifts, and regional
skill-density rankings. Results can be exported as CSV for downstream reporting.

Resources

Next Steps

See juicebox-sdk-patterns for authentication and query builder helpers.

Info
Category Data Science
Name juicebox-core-workflow-b
Version v20260423
Size 3.49KB
Updated At 2026-04-28
Language