| --- |
| license: mit |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-ranking |
| tags: |
| - synthetic |
| - jobs |
| - recruiting |
| - resumes |
| - learning-to-rank |
| pretty_name: Synthetic US Candidate Profiles |
| configs: |
| - config_name: default |
| data_files: profiles.parquet |
| --- |
| |
| # Synthetic US Candidate Profiles |
|
|
| 99,998 synthetic job-seeker profiles covering 803 occupations across the United States, |
| generated with DeepSeek V4 Flash. Built to train a job-ranking cross-encoder, where real |
| candidate data cannot be used. |
|
|
| **No real people.** Every profile is generated. No resumes were scraped, no user records |
| were used, and nothing here maps to a real individual. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| ds = load_dataset("<your-org>/<your-dataset>", split="train") |
| profile = json.loads(ds[0]["profile_json"]) |
| print(profile["identity"], profile["skills"]["primary"]) |
| ``` |
|
|
| ## Schema |
|
|
| Six flat columns for filtering without parsing JSON, plus the full record. |
|
|
| | column | type | description | |
| |---|---|---| |
| | `profile_id` | string | unique identifier | |
| | `source` | string | always `synthetic` | |
| | `cohort` | string | generation cohort (13 values, see below) | |
| | `job_family` | string | one of 14 families | |
| | `seniority` | string | `intern` … `executive` | |
| | `country` | string | always `United States` | |
| | `profile_json` | string | complete profile as a JSON object | |
|
|
| `profile_json` contains: |
|
|
| | key | contents | |
| |---|---| |
| | `identity` | seniority, job_family, years_experience, is_student, is_career_changer | |
| | `location` | city, state, country, open_to_remote, open_to_relocation, preferred_locations | |
| | `objective` | career_interests, desired/min salary, employment_types, target_company_tags | |
| | `eligibility` | work_authorization_countries, sponsorship_needed, legal_status, security_clearance | |
| | `skills` | primary, secondary, languages | |
| | `experiences` | title, company, industry, size, start/end dates, is_current, is_remote | |
| | `educations` | school, degree, field, dates | |
| | `certifications`, `projects` | list records, often empty | |
| | `source_meta` | llm_model, cohort, seed_role, which fields were sampled rather than generated | |
|
|
| `years_experience` is computed from date spans, never asked of the model — LLMs are |
| unreliable at date arithmetic. `eligibility` and `open_to_remote` are **sampled** from a |
| fixed distribution rather than inferred, both for controlled coverage and because guessing |
| someone's visa status is not something a model should be doing. |
|
|
| ## Composition |
|
|
| **Cohorts** — profiles are deliberately not all well-formed. Real signups are often sparse, |
| mid-career-change, or hard to classify. |
|
|
| | cohort | n | | cohort | n | |
| |---|---|---|---|---| |
| | standard | 19,998 | | manager_lead | 7,000 | |
| | entry_level | 13,000 | | over_qualified | 6,000 | |
| | career_changer | 10,000 | | return_to_work | 5,000 | |
| | sparse_profile | 9,000 | | visa_constrained | 5,000 | |
| | senior_specialist | 9,000 | | bootcamp_self_taught | 3,000 | |
| | student_intern | 8,000 | | military_transition | 3,000 | |
| | | | | gig_contract | 2,000 | |
|
|
| **Job families** (14) — other 19.0%, operations 17.7%, software_engineering 15.8%, |
| healthcare 10.3%, research 6.3%, hardware_engineering 6.2%, finance 4.9%, education 3.8%, |
| design 3.7%, marketing 3.3%, legal 3.2%, sales 2.6%, and 2 more. |
|
|
| **Seniority** — senior 31.5%, entry 27.2%, mid 24.8%, junior 5.0%, manager 4.3%, |
| intern 3.2%, principal 2.1%, director 1.4%, staff/executive 0.5%. |
|
|
| **Content** — 803 distinct seed occupations, 66 states/territories (top: California, Texas, |
| Florida, Colorado, North Carolina, Arizona). Median 13 skills, 3 experience entries, |
| 10.2 years experience (p10 1.9, p90 20.2). 69.8% open to remote, 5.1% need sponsorship, |
| median target salary $85,000. |
|
|
| ## How it was built |
|
|
| Roles were enumerated systematically rather than sampled, so coverage is even rather than |
| popularity-weighted: a vocabulary bank was generated per occupation (803 roles → 36,040 |
| distinct skill, tool, specialization, and certification terms), then each profile was seeded |
| with a role, cohort, city, and skill subset before generation at temperature 1.0. Every |
| profile was validated against a JSON schema; 14 were rejected and 2 more dropped in |
| verification. |
|
|
| Generation cost roughly $24 and 140 minutes at 96-way concurrency. |
|
|
| ## Intended use |
|
|
| Training and evaluating candidate–job relevance models — cross-encoder rerankers, |
| bi-encoder retrieval, matching heuristics. Useful anywhere real candidate data is |
| unavailable or shouldn't be used. |
|
|
| ## Limitations |
|
|
| - **Synthetic.** Reflects an LLM's model of what job seekers look like, not the real |
| distribution. Validate on real data before trusting production numbers. |
| - **US only.** Every profile is US-based. Do not expect transfer to other labour markets. |
| - **Round-number bias.** LLM-generated salaries and dates cluster on round values. |
| - **Inherited bias.** Name, location, industry, and seniority associations reflect the |
| generating model's biases. Not suitable for fairness auditing, and any hiring-adjacent |
| use needs its own bias evaluation. |
| - **`state` is a full name** (`"Illinois"`, not `"IL"`). Normalize before joining against |
| systems that store two-letter codes. |
| - **Not a hiring tool.** These are training fixtures, not candidates. |
|
|
