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 L2 · Applied Scientist
Apply applied ai / applied science capability at independently delivers standard work within a team or defined domain.
The five dimensions that change
- Autonomy
- Independent routine ownership
- Scope
- Independently delivers standard work within a team or defined domain.
- Complexity
- Moderate
- Influence
- Immediate team
- Business impact
- Task/team
- Ambiguity
- Moderate
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 L2.
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 L2 scope.
Skills expected at L2
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.
Human8
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalSupporting | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalSupporting | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalSupporting | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalSupporting | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalSupporting | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalSupporting | Applies Structured problem solving to routine situations using established methods and controls. |
Professional7
| Professional | P | Supporting |
|---|---|---|
| AI evaluation governanceData & Analytics Practice | P2FoundationalSupporting | Applies AI evaluation governance to routine situations using established methods and controls. |
| AI governanceData & Analytics Practice | P2FoundationalSupporting | Applies AI governance to routine situations using established methods and controls. |
| Data ethicsData & Analytics Practice | P2FoundationalCore | Applies Data ethics to routine situations using established methods and controls. |
| Experiment designData & Analytics Practice | P2FoundationalCore | Applies Experiment design to routine situations using established methods and controls. |
| Model risk managementData & Analytics Practice | P2FoundationalSupporting | Applies Model risk management to routine situations using established methods and controls. |
| Responsible AI principlesData & Analytics Practice | P2FoundationalSupporting | Applies Responsible AI principles to routine situations using established methods and controls. |
| Statistical inferenceData & Analytics Practice | P2FoundationalCore | Applies Statistical inference to routine situations using established methods and controls. |
Technical10
| Technical | P | Supporting |
|---|---|---|
| Deep learningData Science, ML & AI | P2FoundationalCore | Uses Deep learning for routine tasks with documented patterns and review. |
| LLM evaluation and benchmarkingData Science, ML & AI | P2FoundationalSupporting | Uses LLM evaluation and benchmarking for routine tasks with documented patterns and review. |
| LLM fine-tuning and PEFTData Science, ML & AI | P2FoundationalSupporting | Uses LLM fine-tuning and PEFT for routine tasks with documented patterns and review. |
| Large language modelsData Science, ML & AI | P2FoundationalCore | Uses Large language models for routine tasks with documented patterns and review. |
| Machine learningData Science, ML & AI | P2FoundationalCore | Uses Machine learning for routine tasks with documented patterns and review. |
| Model evaluationData Science, ML & AI | P2FoundationalCore | Uses Model evaluation for routine tasks with documented patterns and review. |
| Multimodal AIData Science, ML & AI | P2FoundationalSupporting | Uses Multimodal AI for routine tasks with documented patterns and review. |
| Natural language processingData Science, ML & AI | P2FoundationalCore | Uses Natural language processing for routine tasks with documented patterns and review. |
| Reinforcement learning for AIData Science, ML & AI | P2FoundationalSupporting | Uses Reinforcement learning for AI for routine tasks with documented patterns and review. |
| Synthetic data generationData Science, ML & AI | P2FoundationalSupporting | Uses Synthetic data generation for routine tasks with documented patterns and review. |
What changes from L2 to L3
Moving from Applied Scientist to Senior 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 L3-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.