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Pipeline run

21e34eeb-442b-4666-96f2-28a6fbf9d4e2

Pipeline LLM cost (USD)
API 1: $0.0042 API 2: $0.0006 API 3: $0.0000 Total: $0.0048

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI/ML Solutions Development
Design and deploy scalable AI/ML models and pipelines, analyze complex data for actionable insights, and troubleshoot data/model/deployment issues. You’ll also review code, document decisions, and work with stakeholders to turn business needs into production-ready solutions.
"Independently design and develop scalable AI/ML solutions using advanced design patterns, models and algorithms to analyse complex datasets and generate actionable insights"
Tech stack maturity
Mainstream Modern
The role and skills center on Python, machine learning, code review, and observability, which are characteristic of a widely adopted modern software/data stack rather than legacy or bleeding-edge technology.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): Transformers, NLP, computer vision, AI, ML, AI/ML, Machine Learning, Deep Learning, Reinforcement Learning
Evidence — skills matched in JD (19)
Machine Learning Deep Learning Natural Language Processing Data Analysis Data Science Statistical Analysis Python Model Deployment Machine Learning Pipelines Data Validation Testing Code Review Pair Programming Documentation Observability Optimization Scientific Computing Decision Modeling Risk Analysis
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Python Programming
Python
Cross-cutting / unaligned
Deep Learning Natural Language Processing Data Analysis Data Science Statistical Analysis Model Deployment Machine Learning Pipelines Data Validation Testing Code Review Pair Programming Documentation Observability Optimization Scientific Computing Decision Modeling Risk Analysis
Show KRA description ↓
• Independently design and develop scalable AI/ML solutions using advanced design patterns, models and algorithms to analyse complex datasets and generate actionable insights • Collaborate with stakeholders to translate business needs into AI/ML solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions • Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes • Investigate and resolve complex challenges in data pipelines, modelling, and deployment to ensure reliable solutions that meet performance benchmarks • Mentor team members through code reviews, model validations, pairing sessions, and knowledge-sharing sessions, and contribute to Communities of Practice • Effectively communicate modelling, infrastructure, and deployment choices to both technical and non-technical stakeholders • Maintain detailed, impactful documentation, covering methodologies, data pipelines, model performance, and key design decisions to enable reproducibility and scalability • Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects • Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams • Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable AI/ML solutions • Mastered Data Analysis, Data Science and and AI & Machine Learning concepts and can demonstrate this skill in complex scenarios • Programming and Statistical Analysis Skills beyond the fundamentals and can demonstrate the skills in most situations without guidance. • To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance: • Data Validation and Testing • Model Deployment • Machine Learning Pipelines • Deep Learning • Natural Language Processing (NPL) • Optimization & Scientific Computing • Decision Modelling and Risk Analysis. • Technical Documentation

Signals

Skill ml-ops-engineer
0.11
Alias data-scientist
1.00
KRA ml-ops-engineer
0.58

Post-classification

Centroidupdated · n=10
Alias collision log
New-role queue
New skills captured15
New KRA capturedyes

Captured for admin review

Deep Learning primary Data Scientist pending
Natural Language Processing primary Data Scientist pending
Data Analysis primary Data Scientist pending
Data Science primary Data Scientist pending
Statistical Analysis primary Data Scientist pending
Model Deployment primary Data Scientist pending
Machine Learning Pipelines primary Data Scientist pending
Data Validation primary Data Scientist pending
Testing primary Data Scientist pending
Pair Programming primary Data Scientist pending
Documentation primary Data Scientist pending
Optimization primary Data Scientist pending
Scientific Computing primary Data Scientist pending
Decision Modeling primary Data Scientist pending
Risk Analysis primary Data Scientist pending
R&R fragment (sim 0.00) Data Scientist pending

• Independently design and develop scalable AI/ML solutions using advanced design patterns, models and algorithms to analyse complex datasets and generate actionable insights • Collaborate with stakeh…

Status: completed Created: 2026-05-27T16:00:11.301582Z Updated: 2026-06-12T15:41:47.378700Z API 3 duration: 38859 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Scientist

CASE A

slug: data-scientist · id: 49 · source: db

Exact alias hit on data-scientist (1.0) — no other alias at this confidence; skill_top ml-ops-engineer 0.11 does not contradict

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Senior AI/ML Scientist(IC) – Global Data Analytics, Technology (Maersk)

This position will be based in India – Bangalore/Pune

A.P. Moller - Maersk

A.P. Moller – Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains.

Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.

The Brief

In this role as an AI/ML Scientist on the Global Data and Analytics (GDA) team, you will support the development of strategic, visibility-driven recommendation systems that serve both internal stakeholders and external customers. This initiative aims to deliver actionable insights that enhance supply chain execution, support strategic decision-making, and enable innovative service offerings.

You should be able to design, develop, and implement machine learning models, conduct deep data analysis, and support decision-making with data-driven insights. Responsibilities include building and validating predictive models, supporting experiment design, and integrating advanced techniques like transformers, GANs, and reinforcement learning into scalable production systems.

The role requires solving complex problems using NLP, deep learning, optimization, and computer vision. You should be comfortable working independently, writing reliable code with automated tests, and contributing to debugging and refinement.

You’ll also document your methods and results clearly and collaborate with cross-functional teams to deliver high-impact AI/ML solutions that align with business objectives and user needs.

What I'll be doing – your accountabilities?

• Independently design and develop scalable AI/ML solutions using advanced design patterns, models and algorithms to analyse complex datasets and generate actionable insights
• Collaborate with stakeholders to translate business needs into AI/ML solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions
• Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes
• Investigate and resolve complex challenges in data pipelines, modelling, and deployment to ensure reliable solutions that meet performance benchmarks
• Mentor team members through code reviews, model validations, pairing sessions, and knowledge-sharing sessions, and contribute to Communities of Practice
• Effectively communicate modelling, infrastructure, and deployment choices to both technical and non-technical stakeholders
• Maintain detailed, impactful documentation, covering methodologies, data pipelines, model performance, and key design decisions to enable reproducibility and scalability
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects
• Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams
• Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable AI/ML solutions


Foundational Skills

• Mastered Data Analysis, Data Science and and AI & Machine Learning concepts and can demonstrate this skill in complex scenarios
• Programming and Statistical Analysis Skills beyond the fundamentals and can demonstrate the skills in most situations without guidance.


Specialized Skills

• To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance:
• Data Validation and Testing
• Model Deployment
• Machine Learning Pipelines
• Deep Learning
• Natural Language Processing (NPL)
• Optimization & Scientific Computing
• Decision Modelling and Risk Analysis.
• Technical Documentation

Qualifications & Requirements

• Bachelor's degree in B.E/BTech, preferably in computer science
• Experience with collaborative development workflow: IDE (Integrated Development Environment), Version control(github), CI/CD (e.g. automated tests in github actions)
• Communicate effectively with technical and non-technical audiences with experience in stakeholder management
• Structured, highly analytical mind-set and excellent problem-solving skills;
• Self-starter, highly motivated & Willing to share knowledge and work as a team.
• An individual who respects the opinion of others; yet can drive a decision though the team;


Preferred Experiences

• 8+ years of years of relevant experience in the field of Data Engineering
• 5+ years of hands-on experience with Apache Spark, Python and SQL
• Experience working with large datasets and big data technologies to train and evaluate machine learning models.
• Experience with containerization: Kubernetes & Docker
• Expertise in building cloud native applications and data pipelines (Azure, Databricks, AWS, GCP) C
• Experience with common dashboarding and API technologies (PowerBI, Superset, Flask, FastAPI, etc


As a performance-oriented company, we strive to always recruit the best person for the job – regardless of gender, age, nationality, sexual orientation or religious beliefs. We are proud of our diversity and see it as a genuine source of strength for building high-performing teams.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing accommodationrequests@maersk.com.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deep Learning Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Natural Language Processing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Analysis Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Science Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Statistical Analysis Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

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)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

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)
Model Deployment Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Machine Learning Pipelines Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Validation Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Testing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Testing Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Code Review Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Code Review id=516 · code-review

Aliases — catalog

  • Code Review (CANONICAL)

Context tags (catalog)

Bitbucket GitHub GitLab PR review approval workflow branch protection code quality diff inline comments linting merge request pair programming pull request review checklist static analysis

Stored enrichment (catalog DB)

Category
SoftSkill
Sub-category
Code Review
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
58
Sub-category id
364
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pair Programming Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Soft Skills
Sub-category
general
Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Documentation Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Soft Skills
Sub-category
general
Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Observability Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Observability id=1581 · observability

Aliases — catalog

  • Observability (CANONICAL)

Context tags (catalog)

Grafana OpenTelemetry Prometheus SLIs SLOs alerting dashboards data visualization distributed systems logging metrics monitoring observability tools root cause analysis tracing

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Observability
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Observability is broadly listed in SRE/DevOps job descriptions and supported by major vendors like Datadog, Grafana, and New Relic, indicating mainstream hiring demand.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1187
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Optimization Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Scientific Computing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Decision Modeling Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Risk Analysis Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
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
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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)
Code Review in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Deep Learning | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Natural Language Processing | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Analysis | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Science | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Statistical Analysis | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Machine Learning Pipelines | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Validation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Testing | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Pair Programming | type=Soft Skills subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Documentation | type=Soft Skills subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Optimization | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Scientific Computing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Decision Modeling | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Risk Analysis | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior AI/ML Scientist(IC) – Global Data Analytics, Technology
CompanyA.P. Moller - Maersk
Experience8+ years of years of relevant experience in the field of Data Engineering
DomainIT Services & Consulting
Location Bangalore, India (null)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "A.P. Moller \u2013 Maersk is",
      "last_5_words": "and simplify its customers\u2019 supply chains."
    },
    "text": "A.P. Moller \u2013 Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers\u2019 supply chains.",
    "word_count": 42
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  "certifications": [],
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        "Tech Consulting",
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      "level": "Bachelor\u0027s",
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  "experience": {
    "max": null,
    "min": 8,
    "raw": "8+ years of years of relevant experience in the field of Data Engineering"
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      "aliases": [],
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      "state": "Maharashtra",
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  ],
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    "AI/ML Scientist",
    "Machine Learning Scientist",
    "Data Scientist"
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  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 10,
      "heading": "What I\u0027ll be doing \u2013 your accountabilities?",
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      "source_marker": {
        "first_5_words": "\u2022 Independently design and develop",
        "last_5_words": "in designing efficient and scalable AI/ML solutions"
      },
      "text": "\u2022 Independently design and develop scalable AI/ML solutions using advanced design patterns, models and algorithms to analyse complex datasets and generate actionable insights\n\u2022 Collaborate with stakeholders to translate business needs into AI/ML solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions\n\u2022 Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes\n\u2022 Investigate and resolve complex challenges in data pipelines, modelling, and deployment to ensure reliable solutions that meet performance benchmarks\n\u2022 Mentor team members through code reviews, model validations, pairing sessions, and knowledge-sharing sessions, and contribute to Communities of Practice\n\u2022 Effectively communicate modelling, infrastructure, and deployment choices to both technical and non-technical stakeholders\n\u2022 Maintain detailed, impactful documentation, covering methodologies, data pipelines, model performance, and key design decisions to enable reproducibility and scalability\n\u2022 Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects\n\u2022 Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams\n\u2022 Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable AI/ML solutions",
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      "bullet_count": 8,
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      "source_marker": {
        "first_5_words": "\u2022 To be able to understand",
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      "word_count": 56
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  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
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      {
        "display_name": "ML Engineer",
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          {
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          },
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
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        "matched_count": null,
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    ],
    "skill_match_roles": [
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      {
        "display_name": "Cyber Security Engineer",
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          "Python"
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      },
      {
        "display_name": "AR/VR Engineer",
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          "Python"
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  },
  "stage4_decision": {
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    "reasoning": "Exact alias hit on data-scientist (1.0) \u2014 no other alias at this confidence; skill_top ml-ops-engineer 0.11 does not contradict",
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}
API 2 — extract-details
{
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2015,
      "existing_alias_text": "Machine Learning",
      "input_term": "Machine Learning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
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          "alias_type": "VERSION",
          "id": 77,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.12",
          "alias_type": "VERSION",
          "id": 78,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.x",
          "alias_type": "VERSION",
          "id": 75,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py",
          "alias_type": "VERSION",
          "id": 2183,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py2",
          "alias_type": "VERSION",
          "id": 68,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py3",
          "alias_type": "VERSION",
          "id": 69,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3",
          "alias_type": "VERSION",
          "id": 2186,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.x",
          "alias_type": "VERSION",
          "id": 2849,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python2",
          "alias_type": "VERSION",
          "id": 70,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3",
          "alias_type": "VERSION",
          "id": 71,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3.x",
          "alias_type": "VERSION",
          "id": 2848,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "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"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "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"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "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": "Model Deployment",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "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": "model-deployment",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Machine Learning Pipelines",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "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": "machine-learning-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Validation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "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": "data-validation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Testing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Testing Tools",
          "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": "testing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Code Review",
          "alias_type": "CANONICAL",
          "id": 864,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 58,
        "display_name": "Code Review",
        "id": 516,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PRACTICE",
        "slug": "code-review",
        "sub_category_id": 364,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Code Review",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Code Review",
      "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": "Pair Programming",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Soft Skills",
          "skill_nature": "PRACTICE",
          "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": "pair-programming",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Documentation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Soft Skills",
          "skill_nature": "PRACTICE",
          "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": "documentation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Observability",
          "alias_type": "CANONICAL",
          "id": 2527,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Observability",
        "id": 1581,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "observability",
        "sub_category_id": 1187,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Operations",
            "id": 143,
            "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
            "slug": "observability-and-operations",
            "source": "db"
          },
          "input_skill": "Observability",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Observability",
      "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": "Optimization",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "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": "optimization",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Scientific Computing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "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": "scientific-computing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Decision Modeling",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "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": "decision-modeling",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Risk Analysis",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "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": "risk-analysis",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Deep Learning",
    "Natural Language Processing",
    "Data Analysis",
    "Data Science",
    "Statistical Analysis",
    "Model Deployment",
    "Machine Learning Pipelines",
    "Data Validation",
    "Testing",
    "Pair Programming",
    "Documentation",
    "Optimization",
    "Scientific Computing",
    "Decision Modeling",
    "Risk Analysis"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Scientist",
    "id": 49,
    "rationale": "Exact alias hit on data-scientist (1.0) \u2014 no other alias at this confidence; skill_top ml-ops-engineer 0.11 does not contradict",
    "role_archetype": "Engineering",
    "slug": "data-scientist",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Deep Learning",
      "tag": "new"
    },
    {
      "skill": "Natural Language Processing",
      "tag": "new"
    },
    {
      "skill": "Data Analysis",
      "tag": "new"
    },
    {
      "skill": "Data Science",
      "tag": "new"
    },
    {
      "skill": "Statistical Analysis",
      "tag": "new"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Model Deployment",
      "tag": "new"
    },
    {
      "skill": "Machine Learning Pipelines",
      "tag": "new"
    },
    {
      "skill": "Data Validation",
      "tag": "new"
    },
    {
      "skill": "Testing",
      "tag": "new"
    },
    {
      "skill": "Code Review",
      "tag": "in_db"
    },
    {
      "skill": "Pair Programming",
      "tag": "new"
    },
    {
      "skill": "Documentation",
      "tag": "new"
    },
    {
      "skill": "Observability",
      "tag": "in_db"
    },
    {
      "skill": "Optimization",
      "tag": "new"
    },
    {
      "skill": "Scientific Computing",
      "tag": "new"
    },
    {
      "skill": "Decision Modeling",
      "tag": "new"
    },
    {
      "skill": "Risk Analysis",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
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LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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