Applied AI / Applied Science
Applied AI / Applied Science is an individual-contributor Data + AI career progressing from guided delivery to enterprise expertise without requiring people management.
- Función
- Data & Analytics
- Arquetipo
- Science
- Nivel más alto
- L7 · Distinguished Applied Scientist
Para qué existe
Applies scientific methods and advanced AI/ML research to product and business problems, bridging experimentation and production impact.
Responsabilidades típicas
Deliver applied ai / applied science outcomes; apply data/AI standards; manage quality, reliability and risk; collaborate across product, engineering, security, legal and business teams; at senior levels shape enterprise standards and strategy.
Dónde se trabaja
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
Cómo avanza la carrera
Associate Applied Scientist → Applied Scientist → Senior Applied Scientist → Staff Applied Scientist → Principal Applied Scientist → Senior Principal Applied Scientist → Distinguished Applied Scientist
Niveles de esta carrera
Seis etapas estándar. La séptima existe solo donde la carrera la contempla.
- L1Associate Applied Scientist
- L2Applied Scientist
- L3Senior Applied Scientist
- L4Staff Applied Scientist
- L5Principal Applied Scientist
- L6Senior Principal Applied Scientist
- L7Distinguished Applied Scientist
Nivel distinguido opcional. No todas las carreras llegan acá.
Qué se espera en L2 · Applied Scientist
Apply applied ai / applied science capability at independently delivers standard work within a team or defined domain.
Las cinco dimensiones que cambian
- Autonomía
- Independent routine ownership
- Alcance
- Independently delivers standard work within a team or defined domain.
- Complejidad
- Moderate
- Influencia
- Immediate team
- Impacto en el negocio
- Task/team
- Ambigüedad
- Moderate
Cómo se ve un buen desempeño
Produces trustworthy applied ai / applied science outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L2.
Evidencia típica
Completed applied ai / applied science work products or production outcomes; documented decisions; measurable improvements in quality, reliability, risk, speed, cost or business outcomes; peer/stakeholder evidence of L2 scope.
Habilidades esperadas en L2
Agrupadas en humanas, profesionales y técnicas. La proficiencia meta usa la escala P1–P7, y cada fila dice qué significa ese nivel para esa habilidad.
Humanas8
| Humanas | P | De apoyo |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalDe apoyo | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalDe apoyo | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Structured problem solving to routine situations using established methods and controls. |
Profesionales7
| Profesionales | P | De apoyo |
|---|---|---|
| AI evaluation governanceData & Analytics Practice | P2FoundationalDe apoyo | Applies AI evaluation governance to routine situations using established methods and controls. |
| AI governanceData & Analytics Practice | P2FoundationalDe apoyo | Applies AI governance to routine situations using established methods and controls. |
| Data ethicsData & Analytics Practice | P2FoundationalCentral | Applies Data ethics to routine situations using established methods and controls. |
| Experiment designData & Analytics Practice | P2FoundationalCentral | Applies Experiment design to routine situations using established methods and controls. |
| Model risk managementData & Analytics Practice | P2FoundationalDe apoyo | Applies Model risk management to routine situations using established methods and controls. |
| Responsible AI principlesData & Analytics Practice | P2FoundationalDe apoyo | Applies Responsible AI principles to routine situations using established methods and controls. |
| Statistical inferenceData & Analytics Practice | P2FoundationalCentral | Applies Statistical inference to routine situations using established methods and controls. |
Técnicas10
| Técnicas | P | De apoyo |
|---|---|---|
| Deep learningData Science, ML & AI | P2FoundationalCentral | Uses Deep learning for routine tasks with documented patterns and review. |
| LLM evaluation and benchmarkingData Science, ML & AI | P2FoundationalDe apoyo | Uses LLM evaluation and benchmarking for routine tasks with documented patterns and review. |
| LLM fine-tuning and PEFTData Science, ML & AI | P2FoundationalDe apoyo | Uses LLM fine-tuning and PEFT for routine tasks with documented patterns and review. |
| Large language modelsData Science, ML & AI | P2FoundationalCentral | Uses Large language models for routine tasks with documented patterns and review. |
| Machine learningData Science, ML & AI | P2FoundationalCentral | Uses Machine learning for routine tasks with documented patterns and review. |
| Model evaluationData Science, ML & AI | P2FoundationalCentral | Uses Model evaluation for routine tasks with documented patterns and review. |
| Multimodal AIData Science, ML & AI | P2FoundationalDe apoyo | Uses Multimodal AI for routine tasks with documented patterns and review. |
| Natural language processingData Science, ML & AI | P2FoundationalCentral | Uses Natural language processing for routine tasks with documented patterns and review. |
| Reinforcement learning for AIData Science, ML & AI | P2FoundationalDe apoyo | Uses Reinforcement learning for AI for routine tasks with documented patterns and review. |
| Synthetic data generationData Science, ML & AI | P2FoundationalDe apoyo | Uses Synthetic data generation for routine tasks with documented patterns and review. |
Qué cambia al pasar de L2 a L3
Moving from Applied Scientist to Senior Applied Scientist means greater autonomy, complexity, scope, influence and evidence of impact—not simply more tools.
Cómo se demuestra que ya estás
Sustained evidence of operating at L3-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
Cómo prepararse
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Carreras cercanas
Calculadas por habilidades compartidas. Es una señal para explorar, no una garantía de contratación ni de elegibilidad.
- AI Governance & Responsible AIData & Analytics13 habilidades compartidas
- AI Platform & MLOps EngineeringData & Analytics12 habilidades compartidas
- Data ScienceData & Analytics13 habilidades compartidas
- Analytics EngineeringData & Analytics10 habilidades compartidas
- Data ArchitectureData & Analytics10 habilidades compartidas
- Machine Learning EngineeringData & Analytics12 habilidades compartidas
Marco de carreras v21, activo desde 17 de agosto de 2026.