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.
- Función
- Data & Analytics
- Arquetipo
- Science
- Nivel más alto
- L7 · Distinguished Data Scientist
Para qué existe
Enables the organization to deliver reliable outcomes in data science by building progressively deeper expertise, judgment, ownership and business impact.
Responsabilidades típicas
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.
Dónde se trabaja
Common in Data & Analytics teams across technology companies, shared-services organizations, consulting firms, multinational operations and other employers that require data science capability.
Cómo avanza la carrera
Associate Data Scientist → Data Scientist → Senior Data Scientist → Staff Data Scientist → Principal Data Scientist → Senior Principal Data Scientist → Distinguished Data Scientist
Niveles de esta carrera
Seis etapas estándar. La séptima existe solo donde la carrera la contempla.
- L1Associate Data Scientist
- L2Data Scientist
- L3Senior Data Scientist
- L4Staff Data Scientist
- L5Principal Data Scientist
- L6Senior Principal Data Scientist
- L7Distinguished Data Scientist
Nivel distinguido opcional. No todas las carreras llegan acá.
Qué se espera en L2 · Data Scientist
Independently delivers standard work within a team or defined domain. The focus is successful individual contribution at this stage, not people management.
Las cinco dimensiones que cambian
- Autonomía
- Works independently on routine work; seeks help for exceptions.
- Alcance
- Owns complete assignments or a defined area.
- Complejidad
- Standard problems with some judgment required.
- Influencia
- Team and routine cross-functional partners.
- Impacto en el negocio
- Predictable delivery, quality and customer/team outcomes.
- Ambigüedad
- Low to moderate.
Cómo se ve un buen desempeño
Data Scientist consistently demonstrates the expected autonomy and judgment for L2, 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.
Evidencia típica
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.
Habilidades esperadas en L2
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.
Humanas11
| Humanas | P | De apoyo |
|---|---|---|
| AccountabilityExecution & Self-Management | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Active listeningCommunication & Language | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| AdaptabilityExecution & Self-Management | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Clear verbal communicationCommunication & Language | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Clear written communicationCommunication & Language | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| CollaborationCollaboration & Relationships | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Continuous learningExecution & Self-Management | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Critical thinkingThinking & Problem Solving | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| ProfessionalismEthics & Professional Conduct | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Structured problem solvingThinking & Problem Solving | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
| Time managementExecution & Self-Management | P3WorkingCentral | Applies the skill independently in normal and moderately complex situations. |
Profesionales6
| Profesionales | P | De apoyo |
|---|---|---|
| Data product managementData & Analytics Practice | P2FoundationalCentral | Applies the skill to routine work with guidance and follows established practices. |
| Data quality managementCybersecurity, Privacy & Data Governance Standards | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
| Data stewardshipCybersecurity, Privacy & Data Governance Standards | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
| Data storytellingData & Analytics Practice | P2FoundationalCentral | Applies the skill to routine work with guidance and follows established practices. |
| User story definitionAgile, Product & Delivery Methods | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
| Value stream managementAgile, Product & Delivery Methods | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
Técnicas4
| Técnicas | P | De apoyo |
|---|---|---|
| Data visualization implementationAnalytics, BI & Data Visualization | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
| Machine learningData Science, ML & AI | P2FoundationalCentral | Applies the skill to routine work with guidance and follows established practices. |
| Python data analysisAnalytics, BI & Data Visualization | P2FoundationalDe apoyo | Applies the skill to routine work with guidance and follows established practices. |
| Unsupervised learningData Science, ML & AI | P2FoundationalCentral | Applies the skill to routine work with guidance and follows established practices. |
Qué cambia al pasar de L2 a L3
Moving from Data Scientist to Senior 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.
Cómo se demuestra que ya estás
Repeatedly performs key aspects of Senior Data Scientist before promotion; demonstrates the required skill increases; handles complex problems requiring analysis and trade-offs.; receives credible stakeholder evidence; shows measurable outcomes at the next-level scope.
Cómo prepararse
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.
Carreras cercanas
Calculadas por habilidades compartidas. Es una señal para explorar, no una garantía de contratación ni de elegibilidad.
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Marco de carreras v21, activo desde 17 de agosto de 2026.