技能 数据科学 进阶候选人数据分析工作流

进阶候选人数据分析工作流

v20260423
juicebox-core-workflow-b
本工作流用于对大规模人才智能数据集进行深度分析。用户可以构建具备多维度筛选条件的自定义查询,执行跨数据集比较(如不同时间段的候选人池对比),并按地域聚合技能密度。这对于需要识别市场趋势和优化招聘策略的招聘团队至关重要。
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概览

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.

信息
分类 数据科学
名称 juicebox-core-workflow-b
版本 v20260423
大小 3.49KB
更新时间 2026-04-28
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