Pipeline run
b464d3e0-1325-48f4-b379-8c7db23f7b01
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
Lead the development and implementation of solutions to global technical services issues and concerns regarding EDC tools like Medidata RAVE or Oracle Inform or Data/Report programming or Visual Analy…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
BI Developer
domain · Data Engineering & Analytics CASE DOMAINslug: bi-developer · id: 147 · source: db
Domain=Data Engineering & Analytics; The JD centers on SAS/Python programming plus Spotfire/Tableau dashboard development and clinical data/reporting support, which best matches a BI Developer role among the candidates.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Overview Lead the development and implementation of solutions to global technical services issues and concerns regarding EDC tools like Medidata RAVE or Oracle Inform or Data/Report programming or Visual Analytics tools like Spotfire or Tableau. Develop/validate Custom/Complex SAS / SDTM datasets and efficiently handle external data and reconciliations. Perform any postproduction changes to the EDC database or enhancements to the SAS programs like SAS edit checks, listings, Protocol Deviations, SAS Datasets etc., Conduct Peer Review/Quality control of study design for assigned projects. Summary Of Responsibilities • Lead the development and implementation of solutions to global technical services issues and concerns regarding SAS or Python or Data/Report programming or Visual Analytics tools like Spotfire or Tableau. • Develop/validate Custom/Complex SAS / Python/SDTM datasets and efficiently handle external data and reconciliations. • Perform any post production changes to the SAS/Python programs edit checks, listings, Protocol Deviations, Datasets etc., • Conduct Peer Review/Quality control of study design for assigned projects. • Lead the development and implementation of Clinical solutions to global technical services issues and concerns regarding SAS programming. • Lead the development of visual analytics dashboard using tools like Spotfire/Tableau. • Complete assigned work utilising SAS, Python, SAAMA or other proprietary software according to Fortrea SOPs, Work Instructions, and project specific guidelines in accordance with Good Clinical Practices. • Perform any post-production changes to the enhancements for SAS programs like SAS edit checks, listings, Protocol Deviations, etc., • With assistance, meet with Data Manager on assigned projects to discuss contractual obligations and timelines. • Act as Subject Matter Expert (SME) and be point of contact for any technical services, issues related to SAS programming. • Serve as Lead Statistical Programmer providing programming support for development and maintenance of SDTM/Client standards datasets. • Develop/validate Custom/Complex SDTM domains and efficiently handle external data and data reconciliations. • Develop and implement SDTM automation/standardization and best programming practices across projects to enhance quality and productivity. • Plan, execute and oversee all programming activities on a study, including but not limited to, resource estimation, meeting timelines, maximizing quality, interaction with other departments and the client, etc. • Exhibit good imparting, analytical skills along with testing, troubleshooting, error fixing, and documentation skills. • Participate in the ongoing review of the processes used to ensure adaptation of best practices. • Conduct Peer Review/Quality control of study design for assigned projects. • Able to work independently and take initiative to accept new challenges in Clinical Programming Applications, also participate in the validation of new or updates to software. • Resource forecasting and allocate activities to the team members on the project. • Generate reports/metrics or demonstrate programming process to sponsors/auditors (if required), assist with the design of study documents. • Perform other duties as assigned by Manager. Learn more about our EEO & Accommodations request here.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Clinical Data Standards
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Clinical Data Management Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Clinical Data Management Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Visualization Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Tableau (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Tableau Software
- License
- proprietary
- Year introduced
- 2003
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Clinical Data Management Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices in Clinical Research
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Data reconciliation (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Data Validation Methodology
- Confidence
- 0.88
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in finance, payments, and ETL JDs; often listed as a core data quality/control requirement alongside SQL and ETL, with many vendor docs and job postings referencing reconciliation workflows.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 108
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Quality and Reconciliation Catalog dimension db id 27
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Quality and Reconciliation
data-quality-and-reconciliation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices in Clinical Research
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices in Clinical Research
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Software Development Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices in Clinical Research
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Clinical Research Standards
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- SOPs (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Standard Operating Procedure
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Standard operating procedures are a common requirement in ops, QA, and regulated-industry job descriptions; they’re broadly used for process consistency and compliance rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 629
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Operational Automation and Runbooks Catalog dimension db id 222
Library dimension (catalog)
Roles linked in library: MLOps Engineer
-
Release Documentation and Runbooks Catalog dimension db id 158
Library dimension (catalog)
Roles linked in library: DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Operational Automation and Runbooks
operational-automation-and-runbooks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Release Documentation and Runbooks
release-documentation-and-runbooks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Documentation Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Tableau | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Reconciliation | in_db |
Data Quality and Reconciliation
data-quality-and-reconciliation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SOPs | in_db |
Operational Automation and Runbooks
operational-automation-and-runbooks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SOPs | in_db |
Release Documentation and Runbooks
release-documentation-and-runbooks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | SAS | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SDTM | type=Clinical Data Standards subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Medidata RAVE | type=Clinical Data Management Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | Oracle Inform | type=Clinical Data Management Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | Spotfire | type=Data Visualization Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SAAMA | type=Clinical Data Management Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | Clinical Programming | type=Practices in Clinical Research subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Quality Control | type=Practices in Clinical Research subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Peer Review | type=Practices in Clinical Research subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Automation | type=Software Development Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Standardization | type=Practices in Clinical Research subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Good Clinical Practices | type=Clinical Research Standards subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Work Instructions | type=Documentation Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Fortrea",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"HealthTech",
"Clinical Services"
],
"domain": "Healthcare"
},
"secondary": null
},
"education": [],
"experience": null,
"job_locations": [],
"role": "Lead Statistical Programmer",
"role_aliases": [
"Statistical Programmer",
"Lead Programmer",
"SAS Programmer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Job Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Lead the development and implementation",
"last_5_words": "for assigned projects."
},
"text": "Lead the development and implementation of solutions to global technical services issues and concerns regarding EDC tools like Medidata RAVE or Oracle Inform or Data/Report programming or Visual Analytics tools like Spotfire or Tableau. Develop/validate Custom/Complex SAS / SDTM datasets and efficiently handle external data and reconciliations. Perform any postproduction changes to the EDC database or enhancements to the SAS programs like SAS edit checks, listings, Protocol Deviations, SAS Datasets etc., Conduct Peer Review/Quality control of study design for assigned projects.",
"word_count": 66
},
{
"bullet_count": 20,
"heading": "Summary Of Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Lead the development and implementation",
"last_5_words": "as assigned by Manager."
},
"text": "\u2022 Lead the development and implementation of solutions to global technical services issues and concerns regarding SAS or Python or Data/Report programming or Visual Analytics tools like Spotfire or Tableau.\n\u2022 Develop/validate Custom/Complex SAS / Python/SDTM datasets and efficiently handle external data and reconciliations.\n\u2022 Perform any post production changes to the SAS/Python programs edit checks, listings, Protocol Deviations, Datasets etc.,\n\u2022 Conduct Peer Review/Quality control of study design for assigned projects.\n\u2022 Lead the development and implementation of Clinical solutions to global technical services issues and concerns regarding SAS programming.\n\u2022 Lead the development of visual analytics dashboard using tools like Spotfire/Tableau.\n\u2022 Complete assigned work utilising SAS, Python, SAAMA or other proprietary software according to Fortrea SOPs, Work Instructions, and project specific guidelines in accordance with Good Clinical Practices.\n\u2022 Perform any post-production changes to the enhancements for SAS programs like SAS edit checks, listings, Protocol Deviations, etc.,\n\u2022 With assistance, meet with Data Manager on assigned projects to discuss contractual obligations and timelines.\n\u2022 Act as Subject Matter Expert (SME) and be point of contact for any technical services, issues related to SAS programming.\n\u2022 Serve as Lead Statistical Programmer providing programming support for development and maintenance of SDTM/Client standards datasets.\n\u2022 Develop/validate Custom/Complex SDTM domains and efficiently handle external data and data reconciliations.\n\u2022 Develop and implement SDTM automation/standardization and best programming practices across projects to enhance quality and productivity.\n\u2022 Plan, execute and oversee all programming activities on a study, including but not limited to, resource estimation, meeting timelines, maximizing quality, interaction with other departments and the client, etc.\n\u2022 Exhibit good imparting, analytical skills along with testing, troubleshooting, error fixing, and documentation skills.\n\u2022 Participate in the ongoing review of the processes used to ensure adaptation of best practices.\n\u2022 Conduct Peer Review/Quality control of study design for assigned projects.\n\u2022 Able to work independently and take initiative to accept new challenges in Clinical Programming Applications, also participate in the validation of new or updates to software.\n\u2022 Resource forecasting and allocate activities to the team members on the project.\n\u2022 Generate reports/metrics or demonstrate programming process to sponsors/auditors (if required), assist with the design of study documents.\n\u2022 Perform other duties as assigned by Manager.",
"word_count": 392
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "SAS"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "SDTM"
},
{
"is_primary": true,
"skill_name": "Medidata RAVE"
},
{
"is_primary": true,
"skill_name": "Oracle Inform"
},
{
"is_primary": true,
"skill_name": "Spotfire"
},
{
"is_primary": true,
"skill_name": "Tableau"
},
{
"is_primary": false,
"skill_name": "SAAMA"
},
{
"is_primary": true,
"skill_name": "Clinical Programming"
},
{
"is_primary": true,
"skill_name": "Data Reconciliation"
},
{
"is_primary": true,
"skill_name": "Quality Control"
},
{
"is_primary": true,
"skill_name": "Peer Review"
},
{
"is_primary": true,
"skill_name": "Automation"
},
{
"is_primary": true,
"skill_name": "Standardization"
},
{
"is_primary": true,
"skill_name": "Good Clinical Practices"
},
{
"is_primary": true,
"skill_name": "SOPs"
},
{
"is_primary": true,
"skill_name": "Work Instructions"
}
],
"jd_role": {
"display_name": "Lead Statistical Programmer",
"rationale": null,
"role_aliases": [
"Statistical Programmer",
"Lead Programmer",
"SAS Programmer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Fortrea",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"HealthTech",
"Clinical Services"
],
"domain": "Healthcare"
},
"secondary": null
},
"education": [],
"experience": null,
"job_locations": [],
"role": "Lead Statistical Programmer",
"role_aliases": [
"Statistical Programmer",
"Lead Programmer",
"SAS Programmer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Job Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Lead the development and implementation",
"last_5_words": "for assigned projects."
},
"text": "Lead the development and implementation of solutions to global technical services issues and concerns regarding EDC tools like Medidata RAVE or Oracle Inform or Data/Report programming or Visual Analytics tools like Spotfire or Tableau. Develop/validate Custom/Complex SAS / SDTM datasets and efficiently handle external data and reconciliations. Perform any postproduction changes to the EDC database or enhancements to the SAS programs like SAS edit checks, listings, Protocol Deviations, SAS Datasets etc., Conduct Peer Review/Quality control of study design for assigned projects.",
"word_count": 66
},
{
"bullet_count": 20,
"heading": "Summary Of Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Lead the development and implementation",
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API 2 — extract-details
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"dimensions": [],
"input_skill": "Standardization",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Practices in Clinical Research",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "standardization",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Good Clinical Practices",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Clinical Research Standards",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "good-clinical-practices",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "SOPs",
"alias_type": "CANONICAL",
"id": 1459,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "SOPs",
"id": 897,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "sops",
"sub_category_id": 629,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Operational Automation and Runbooks",
"id": 222,
"rationale": "Automation patterns, runbooks, and procedures used to keep model operations repeatable and recoverable. This cluster covers the day-to-day operational glue that turns release policy into executable steps.",
"slug": "operational-automation-and-runbooks",
"source": "db"
},
"input_skill": "SOPs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Release Documentation and Runbooks",
"id": 158,
"rationale": "Operational documents that explain how to deploy, recover, and hand off systems safely. This is a coherent dimension because DevOps work depends on repeatable procedures and clear escalation paths.",
"slug": "release-documentation-and-runbooks",
"source": "db"
},
"input_skill": "SOPs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "SOPs",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Work Instructions",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Documentation Practices",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "work-instructions",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"SAS",
"SDTM",
"Medidata RAVE",
"Oracle Inform",
"Spotfire",
"SAAMA",
"Clinical Programming",
"Quality Control",
"Peer Review",
"Automation",
"Standardization",
"Good Clinical Practices",
"Work Instructions"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "BI Developer",
"id": 147,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on SAS/Python programming plus Spotfire/Tableau dashboard development and clinical data/reporting support, which best matches a BI Developer role among the candidates.",
"role_archetype": null,
"slug": "bi-developer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "SAS",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "SDTM",
"tag": "new"
},
{
"skill": "Medidata RAVE",
"tag": "new"
},
{
"skill": "Oracle Inform",
"tag": "new"
},
{
"skill": "Spotfire",
"tag": "new"
},
{
"skill": "Tableau",
"tag": "in_db"
},
{
"skill": "SAAMA",
"tag": "new"
},
{
"skill": "Clinical Programming",
"tag": "new"
},
{
"skill": "Data Reconciliation",
"tag": "in_db"
},
{
"skill": "Quality Control",
"tag": "new"
},
{
"skill": "Peer Review",
"tag": "new"
},
{
"skill": "Automation",
"tag": "new"
},
{
"skill": "Standardization",
"tag": "new"
},
{
"skill": "Good Clinical Practices",
"tag": "new"
},
{
"skill": "SOPs",
"tag": "in_db"
},
{
"skill": "Work Instructions",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"dimension_id": 31,
"input_skill": "Tableau",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 150,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Quality and Reconciliation",
"id": 27,
"rationale": "Validation and reconciliation practices that ensure data is accurate, complete, and trustworthy. This includes rule-based checks, anomaly detection, cross-system reconciliation, and failure triage.",
"slug": "data-quality-and-reconciliation",
"source": "db"
},
"dimension_id": 27,
"input_skill": "Data Reconciliation",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 133,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Operational Automation and Runbooks",
"id": 222,
"rationale": "Automation patterns, runbooks, and procedures used to keep model operations repeatable and recoverable. This cluster covers the day-to-day operational glue that turns release policy into executable steps.",
"slug": "operational-automation-and-runbooks",
"source": "db"
},
"dimension_id": 222,
"input_skill": "SOPs",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 897,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 147,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Release Documentation and Runbooks",
"id": 158,
"rationale": "Operational documents that explain how to deploy, recover, and hand off systems safely. This is a coherent dimension because DevOps work depends on repeatable procedures and clear escalation paths.",
"slug": "release-documentation-and-runbooks",
"source": "db"
},
"dimension_id": 158,
"input_skill": "SOPs",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 897,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "b464d3e0-1325-48f4-b379-8c7db23f7b01"
}