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 L1 · Associate Applied Scientist
Apply applied ai / applied science capability at learns the discipline and executes defined work with guidance.
The five dimensions that change
- Autonomy
- Close guidance
- Scope
- Learns the discipline and executes defined work with guidance.
- Complexity
- Defined
- Influence
- Immediate team
- Business impact
- Task/team
- Ambiguity
- Low
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 L1.
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 L1 scope.
Skills expected at L1
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.
Human6
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Clear verbal communication; works with close guidance. |
| Clear written communicationCommunication & Language | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Clear written communication; works with close guidance. |
| CollaborationCollaboration & Relationships | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Collaboration; works with close guidance. |
| Critical thinkingThinking & Problem Solving | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Critical thinking; works with close guidance. |
| Cross-functional collaborationCollaboration & Relationships | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Cross-functional collaboration; works with close guidance. |
| Structured problem solvingThinking & Problem Solving | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Structured problem solving; works with close guidance. |
Professional6
| Professional | P | Supporting |
|---|---|---|
| AI governanceData & Analytics Practice | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of AI governance; works with close guidance. |
| Data ethicsData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of Data ethics; works with close guidance. |
| Experiment designData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of Experiment design; works with close guidance. |
| Model risk managementData & Analytics Practice | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Model risk management; works with close guidance. |
| Responsible AI principlesData & Analytics Practice | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Responsible AI principles; works with close guidance. |
| Statistical inferenceData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of Statistical inference; works with close guidance. |
Technical8
| Technical | P | Supporting |
|---|---|---|
| Deep learningData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Deep learning; performs only guided exercises. |
| LLM evaluation and benchmarkingData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of LLM evaluation and benchmarking; performs only guided exercises. |
| LLM fine-tuning and PEFTData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of LLM fine-tuning and PEFT; performs only guided exercises. |
| Large language modelsData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Large language models; performs only guided exercises. |
| Machine learningData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Machine learning; performs only guided exercises. |
| Model evaluationData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of Model evaluation; performs only guided exercises. |
| Natural language processingData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Natural language processing; performs only guided exercises. |
| Reinforcement learning for AIData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of Reinforcement learning for AI; performs only guided exercises. |
What changes from L1 to L2
Moving from Associate Applied Scientist to 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 L2-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.