Domain
Machine Learning & AI
Skill Profile
Sequence labeling, SpaCy, custom entities, BIO tagging, tokenization
Roles
1
where this skill appears
Levels
5
structured growth path
Mandatory requirements
5
the other 0 optional
Machine Learning & AI
Natural Language Processing
3/17/2026
Choose your current level and compare expectations. The items below show what to cover to advance to the next level.
The table shows how skill depth grows from Junior to Principal. Click a row to see details.
| Role | Required | Description |
|---|---|---|
| NLP Engineer | Required | Knows NER basics: entity types (PER, ORG, LOC), BIO tagging, basic approaches. Applies pre-trained spaCy NER models and evaluates quality via F1-score. |
| Role | Required | Description |
|---|---|---|
| NLP Engineer | Required | Independently trains and fine-tunes NER models for domain-specific tasks. Annotates data, configures BIO/BILOU schemes, trains models on spaCy and Hugging Face transformers. |
| Role | Required | Description |
|---|---|---|
| NLP Engineer | Required | Designs production NER systems: multi-model ensemble, active learning for annotation, nested NER, cross-lingual transfer. Optimizes for high accuracy on domain-specific data. |
| Role | Required | Description |
|---|---|---|
| NLP Engineer | Required | Defines NER strategy for the team. Establishes guidelines for annotation, model selection, evaluation methodology. Coordinates annotator work and ensures labeling consistency. |
| Role | Required | Description |
|---|---|---|
| NLP Engineer | Required | Shapes enterprise NER strategy for the organization. Defines unified entity taxonomy, cross-domain NER approaches, and quality assurance standards for all company NER systems. |