Data Science
Data Science is an individual-contributor career path within Data & Analytics. Professionals progress from guided execution to independent delivery, senior problem solving, cross-team leadership and enterprise-level expertise without requiring people management.
- Function
- Data & Analytics
- Archetype
- Science
- Highest level
- L7 · Distinguished Data Scientist
Why it exists
Enables the organization to deliver reliable outcomes in data science by building progressively deeper expertise, judgment, ownership and business impact.
Typical responsibilities
Execute discipline-specific work; apply professional standards; solve increasingly complex problems; collaborate with stakeholders; improve quality and efficiency; share expertise; at senior levels, shape practices and decisions beyond the immediate team.
Where the work happens
Common in Data & Analytics teams across technology companies, shared-services organizations, consulting firms, multinational operations and other employers that require data science capability.
How the career progresses
Associate Data Scientist → Data Scientist → Senior Data Scientist → Staff Data Scientist → Principal Data Scientist → Senior Principal Data Scientist → Distinguished Data Scientist
Levels in this career
Six standard stages. The seventh exists only where the career provides for it.
- L1Associate Data Scientist
- L2Data Scientist
- L3Senior Data Scientist
- L4Staff Data Scientist
- L5Principal Data Scientist
- L6Senior Principal Data Scientist
- L7Distinguished Data Scientist
Optional distinguished level. Not every career reaches it.
What is expected at L1 · Associate Data Scientist
Learns the discipline; executes defined work with guidance. The focus is successful individual contribution at this stage, not people management.
The five dimensions that change
- Autonomy
- Works with regular guidance and review.
- Scope
- Defined tasks within the immediate team.
- Complexity
- Known problems with established approaches.
- Influence
- Primarily peers and direct stakeholders.
- Business impact
- Reliable individual contribution and learning velocity.
- Ambiguity
- Low; requirements are generally clear.
What good looks like
Associate Data Scientist consistently demonstrates the expected autonomy and judgment for L1, delivers outcomes appropriate to the scope of the role, applies required Human, Professional and Technical skills at the mapped proficiency, and produces evidence of impact rather than relying on tenure alone.
Typical evidence
Completed work with measurable quality/outcome; stakeholder feedback; examples of problems solved and decisions made; reusable artifacts or improvements; demonstrated skill proficiency; mentoring/influence evidence at senior levels.
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.
Human8
| Human | P | Supporting |
|---|---|---|
| AccountabilityExecution & Self-Management | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| Active listeningCommunication & Language | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| AdaptabilityExecution & Self-Management | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| Clear verbal communicationCommunication & Language | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| Clear written communicationCommunication & Language | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| CollaborationCollaboration & Relationships | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| Critical thinkingThinking & Problem Solving | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
| Time managementExecution & Self-Management | P2FoundationalCore | Applies the skill to routine work with guidance and follows established practices. |
Professional6
| Professional | P | Supporting |
|---|---|---|
| Data product managementData & Analytics Practice | P1AwarenessCore | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Data quality managementCybersecurity, Privacy & Data Governance Standards | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Data stewardshipCybersecurity, Privacy & Data Governance Standards | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Data storytellingData & Analytics Practice | P1AwarenessCore | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| User story definitionAgile, Product & Delivery Methods | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Value stream managementAgile, Product & Delivery Methods | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
Technical4
| Technical | P | Supporting |
|---|---|---|
| Data visualization implementationAnalytics, BI & Data Visualization | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Machine learningData Science, ML & AI | P1AwarenessCore | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Python data analysisAnalytics, BI & Data Visualization | P1AwarenessSupporting | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
| Unsupervised learningData Science, ML & AI | P1AwarenessCore | Understands the concept, terminology and when the skill is used; performs only with close guidance. |
What changes from L1 to L2
Moving from Associate Data Scientist to Data Scientist means demonstrating sustained performance at a larger scope with greater autonomy, complexity, influence and business impact—not simply spending more time in role.
How readiness is shown
Repeatedly performs key aspects of Data Scientist before promotion; demonstrates the required skill increases; handles standard problems with some judgment required.; receives credible stakeholder evidence; shows measurable outcomes at the next-level scope.
How to prepare
Take stretch assignments at the next-level scope; deepen the listed skill gaps; seek feedback from experienced practitioners; document measurable outcomes and decisions; mentor/share knowledge where appropriate; pursue relevant learning or certification when it strengthens capability.
Adjacent careers
Computed from shared skills. It is a signal for exploring, not a hiring or eligibility guarantee.
- AI / Generative AI EngineeringData & Analytics51 shared skills
- Data EngineeringData & Analytics50 shared skills
- Machine Learning EngineeringData & Analytics48 shared skills
- Data AnalyticsData & Analytics50 shared skills
- Business IntelligenceData & Analytics50 shared skills
- Data Governance & StewardshipData & Analytics42 shared skills
Career framework v21, active since August 17, 2026.