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 L5 · Principal Applied Scientist
Apply applied ai / applied science capability at shapes standards, architecture or operating practice across a broad domain.
Las cinco dimensiones que cambian
- Autonomía
- Domain-wide authority
- Alcance
- Shapes standards, architecture or operating practice across a broad domain.
- Complejidad
- Systemic
- Influencia
- Enterprise / industry
- Impacto en el negocio
- Enterprise / industry
- Ambigüedad
- High
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 L5.
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 L5 scope.
Habilidades esperadas en L5
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.
Humanas10
| Humanas | P | De apoyo |
|---|---|---|
| Clear verbal communicationCommunication & Language | P5AdvancedDe apoyo | Defines advanced approaches for Clear verbal communication and shapes standards across a domain. |
| Clear written communicationCommunication & Language | P5AdvancedDe apoyo | Defines advanced approaches for Clear written communication and shapes standards across a domain. |
| CollaborationCollaboration & Relationships | P5AdvancedDe apoyo | Defines advanced approaches for Collaboration and shapes standards across a domain. |
| Critical thinkingThinking & Problem Solving | P5AdvancedDe apoyo | Defines advanced approaches for Critical thinking and shapes standards across a domain. |
| Cross-functional collaborationCollaboration & Relationships | P5AdvancedDe apoyo | Defines advanced approaches for Cross-functional collaboration and shapes standards across a domain. |
| Learning agilityExecution & Self-Management | P5AdvancedDe apoyo | Defines advanced approaches for Learning agility and shapes standards across a domain. |
| MentoringCollaboration & Relationships | P5AdvancedDe apoyo | Defines advanced approaches for Mentoring and shapes standards across a domain. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P5AdvancedDe apoyo | Defines advanced approaches for Stakeholder alignment and shapes standards across a domain. |
| Strategic thinkingThinking & Problem Solving | P5AdvancedDe apoyo | Defines advanced approaches for Strategic thinking and shapes standards across a domain. |
| Structured problem solvingThinking & Problem Solving | P5AdvancedDe apoyo | Defines advanced approaches for Structured problem solving and shapes standards across a domain. |
Profesionales7
| Profesionales | P | De apoyo |
|---|---|---|
| AI evaluation governanceData & Analytics Practice | P5AdvancedDe apoyo | Defines advanced approaches for AI evaluation governance and shapes standards across a domain. |
| AI governanceData & Analytics Practice | P5AdvancedDe apoyo | Defines advanced approaches for AI governance and shapes standards across a domain. |
| Data ethicsData & Analytics Practice | P5AdvancedCentral | Defines advanced approaches for Data ethics and shapes standards across a domain. |
| Experiment designData & Analytics Practice | P5AdvancedCentral | Defines advanced approaches for Experiment design and shapes standards across a domain. |
| Model risk managementData & Analytics Practice | P5AdvancedDe apoyo | Defines advanced approaches for Model risk management and shapes standards across a domain. |
| Responsible AI principlesData & Analytics Practice | P5AdvancedDe apoyo | Defines advanced approaches for Responsible AI principles and shapes standards across a domain. |
| Statistical inferenceData & Analytics Practice | P5AdvancedCentral | Defines advanced approaches for Statistical inference and shapes standards across a domain. |
Técnicas14
| Técnicas | P | De apoyo |
|---|---|---|
| Deep learningData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using Deep learning and establishes reusable patterns across teams. |
| JupyterAnalytics, BI & Data Visualization | P5AdvancedDe apoyo | Designs advanced approaches using Jupyter and establishes reusable patterns across teams. |
| LLM evaluation and benchmarkingData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using LLM evaluation and benchmarking and establishes reusable patterns across teams. |
| LLM fine-tuning and PEFTData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using LLM fine-tuning and PEFT and establishes reusable patterns across teams. |
| Large language modelsData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using Large language models and establishes reusable patterns across teams. |
| Machine learningData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using Machine learning and establishes reusable patterns across teams. |
| Model evaluationData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using Model evaluation and establishes reusable patterns across teams. |
| Multimodal AIData Science, ML & AI | P5AdvancedDe apoyo | Designs advanced approaches using Multimodal AI and establishes reusable patterns across teams. |
| Natural language processingData Science, ML & AI | P5AdvancedCentral | Designs advanced approaches using Natural language processing and establishes reusable patterns across teams. |
| PyTorchData Science, ML & AI | P5AdvancedDe apoyo | Designs advanced approaches using PyTorch and establishes reusable patterns across teams. |
| Python data analysisAnalytics, BI & Data Visualization | P5AdvancedDe apoyo | Designs advanced approaches using Python data analysis and establishes reusable patterns across teams. |
| Reinforcement learning for AIData Science, ML & AI | P5AdvancedDe apoyo | Designs advanced approaches using Reinforcement learning for AI and establishes reusable patterns across teams. |
| Synthetic data generationData Science, ML & AI | P5AdvancedDe apoyo | Designs advanced approaches using Synthetic data generation and establishes reusable patterns across teams. |
| TensorFlowData Science, ML & AI | P5AdvancedDe apoyo | Designs advanced approaches using TensorFlow and establishes reusable patterns across teams. |
Qué cambia al pasar de L5 a L6
Moving from Principal Applied Scientist to Senior Principal 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 L6-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.