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}%)`)
);
