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.
- Function
- Data & Analytics
- Archetype
- Science
- Highest level
- L7 · Distinguished Applied Scientist
Why it exists
Applies scientific methods and advanced AI/ML research to product and business problems, bridging experimentation and production impact.
Typical responsibilities
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.
Where the work happens
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
How the career progresses
Associate Applied Scientist → Applied Scientist → Senior Applied Scientist → Staff Applied Scientist → Principal Applied Scientist → Senior Principal Applied Scientist → Distinguished Applied Scientist
Levels in this career
Six standard stages. The seventh exists only where the career provides for it.
- L1Associate Applied Scientist
- L2Applied Scientist
- L3Senior Applied Scientist
- L4Staff Applied Scientist
- L5Principal Applied Scientist
- L6Senior Principal Applied Scientist
- L7Distinguished Applied Scientist
Optional distinguished level. Not every career reaches it.
What is expected at L5 · Principal Applied Scientist
Apply applied ai / applied science capability at shapes standards, architecture or operating practice across a broad domain.
The five dimensions that change
- Autonomy
- Domain-wide authority
- Scope
- Shapes standards, architecture or operating practice across a broad domain.
- Complexity
- Systemic
- Influence
- Enterprise / industry
- Business impact
- Enterprise / industry
- Ambiguity
- High
What good looks like
Produces trustworthy applied ai / applied science outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L5.
Typical evidence
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.
Skills expected at L5
Grouped as human, professional and technical. Target proficiency uses the P1–P7 scale, and each row says what that level means for that particular skill.
Human10
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P5AdvancedSupporting | Defines advanced approaches for Clear verbal communication and shapes standards across a domain. |
| Clear written communicationCommunication & Language | P5AdvancedSupporting | Defines advanced approaches for Clear written communication and shapes standards across a domain. |
| CollaborationCollaboration & Relationships | P5AdvancedSupporting | Defines advanced approaches for Collaboration and shapes standards across a domain. |
| Critical thinkingThinking & Problem Solving | P5AdvancedSupporting | Defines advanced approaches for Critical thinking and shapes standards across a domain. |
| Cross-functional collaborationCollaboration & Relationships | P5AdvancedSupporting | Defines advanced approaches for Cross-functional collaboration and shapes standards across a domain. |
| Learning agilityExecution & Self-Management | P5AdvancedSupporting | Defines advanced approaches for Learning agility and shapes standards across a domain. |
| MentoringCollaboration & Relationships | P5AdvancedSupporting | Defines advanced approaches for Mentoring and shapes standards across a domain. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P5AdvancedSupporting | Defines advanced approaches for Stakeholder alignment and shapes standards across a domain. |
| Strategic thinkingThinking & Problem Solving | P5AdvancedSupporting | Defines advanced approaches for Strategic thinking and shapes standards across a domain. |
| Structured problem solvingThinking & Problem Solving | P5AdvancedSupporting | Defines advanced approaches for Structured problem solving and shapes standards across a domain. |
Professional7
| Professional | P | Supporting |
|---|---|---|
| AI evaluation governanceData & Analytics Practice | P5AdvancedSupporting | Defines advanced approaches for AI evaluation governance and shapes standards across a domain. |
| AI governanceData & Analytics Practice | P5AdvancedSupporting | Defines advanced approaches for AI governance and shapes standards across a domain. |
| Data ethicsData & Analytics Practice | P5AdvancedCore | Defines advanced approaches for Data ethics and shapes standards across a domain. |
| Experiment designData & Analytics Practice | P5AdvancedCore | Defines advanced approaches for Experiment design and shapes standards across a domain. |
| Model risk managementData & Analytics Practice | P5AdvancedSupporting | Defines advanced approaches for Model risk management and shapes standards across a domain. |
| Responsible AI principlesData & Analytics Practice | P5AdvancedSupporting | Defines advanced approaches for Responsible AI principles and shapes standards across a domain. |
| Statistical inferenceData & Analytics Practice | P5AdvancedCore | Defines advanced approaches for Statistical inference and shapes standards across a domain. |
Technical14
| Technical | P | Supporting |
|---|---|---|
| Deep learningData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using Deep learning and establishes reusable patterns across teams. |
| JupyterAnalytics, BI & Data Visualization | P5AdvancedSupporting | Designs advanced approaches using Jupyter and establishes reusable patterns across teams. |
| LLM evaluation and benchmarkingData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using LLM evaluation and benchmarking and establishes reusable patterns across teams. |
| LLM fine-tuning and PEFTData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using LLM fine-tuning and PEFT and establishes reusable patterns across teams. |
| Large language modelsData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using Large language models and establishes reusable patterns across teams. |
| Machine learningData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using Machine learning and establishes reusable patterns across teams. |
| Model evaluationData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using Model evaluation and establishes reusable patterns across teams. |
| Multimodal AIData Science, ML & AI | P5AdvancedSupporting | Designs advanced approaches using Multimodal AI and establishes reusable patterns across teams. |
| Natural language processingData Science, ML & AI | P5AdvancedCore | Designs advanced approaches using Natural language processing and establishes reusable patterns across teams. |
| PyTorchData Science, ML & AI | P5AdvancedSupporting | Designs advanced approaches using PyTorch and establishes reusable patterns across teams. |
| Python data analysisAnalytics, BI & Data Visualization | P5AdvancedSupporting | Designs advanced approaches using Python data analysis and establishes reusable patterns across teams. |
| Reinforcement learning for AIData Science, ML & AI | P5AdvancedSupporting | Designs advanced approaches using Reinforcement learning for AI and establishes reusable patterns across teams. |
| Synthetic data generationData Science, ML & AI | P5AdvancedSupporting | Designs advanced approaches using Synthetic data generation and establishes reusable patterns across teams. |
| TensorFlowData Science, ML & AI | P5AdvancedSupporting | Designs advanced approaches using TensorFlow and establishes reusable patterns across teams. |
What changes from L5 to 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.
How readiness is shown
Sustained evidence of operating at L6-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
How to prepare
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Adjacent careers
Computed from shared skills. It is a signal for exploring, not a hiring or eligibility guarantee.
- AI Governance & Responsible AIData & Analytics13 shared skills
- AI Platform & MLOps EngineeringData & Analytics12 shared skills
- Data ScienceData & Analytics13 shared skills
- Analytics EngineeringData & Analytics10 shared skills
- Data ArchitectureData & Analytics10 shared skills
- Machine Learning EngineeringData & Analytics12 shared skills
Career framework v21, active since August 17, 2026.