Data Engineering
Data Science
You'll build predictive models that reshape business strategy, then scale teams who turn algorithms into competitive advantages. This path creates CDOs who speak both machine learning and boardroom fluently.
The Career Arc
Rotational · L1–L3
Build the Data Science craft. Prove you can wield the tools of Data Engineering.
Transformational · L4–L7
Deliver Data Science outcomes — each Data Engineering tour at this altitude has a defined mission and success criteria.
- L4 : Lead data science projects and mentor others
- L5 : Drive data science practices across the org
- L6 : Set data science direction company-wide
- L7 : Shape the company's data science vision
Manage a Team?
Great Data Science managers are practitioners first. The Data Engineering IC responsibilities in L4–L7 are your foundation — your management responsibilities are additive:
- • Hire people you'd trust locked in a room—skills matter, but trust matters more
- • Build a team that balances technical depth with business curiosity
- • Run 1:1s that connect data work to business impact—not just sprint tickets
- • Give feedback that builds both technical and communication skills
- • Remove blockers—fight for data access, compute resources, and stakeholder clarity
Foundational · L8–L9
Shape the Data Engineering organization from the Data Science chair — build institutions, not just products.
L1 — Associate Data Scientist Rotational
Mission
Learn data science through analysis projects
This tour of duty
Complete your first analysis that influences a decision
Own the outcomes
- • Learn statistical analysis and data science fundamentals
- • Write exploratory analysis code under guidance
- • Support data preparation and feature engineering
- • Build foundational knowledge of statistical methods
- • Participate in analysis review discussions
- • Document analysis approaches and findings
Data Science at L1 — the competency bar
AI in this role
- • Generating exploratory analysis code
- • Debugging statistical issues
- • Writing analysis documentation
L2 — Junior Data Scientist Rotational
Mission
Conduct analyses that inform decisions
This tour of duty
Own analyses that stakeholders rely on
Own the outcomes
- • Conduct analyses that inform business decisions
- • Build statistical models for defined problems
- • Create visualizations that communicate findings
- • Collaborate with stakeholders on analytical questions
- • Design and analyze A/B experiments
- • Create analysis documentation and reports
Data Science at L2 — the competency bar
AI in this role
- • Drafting analysis plans
- • Analyzing data patterns
- • Generating visualizations
L3 — Senior Data Scientist Rotational
Mission
Own analytical domains with statistical rigor
This tour of duty
Lead a project that delivers measurable business impact
Own the outcomes
- • Own analytical domains with statistical rigor
- • Design and build predictive models
- • Define experimental methodology and analysis
- • Partner with business teams on complex questions
- • Build reusable analytical frameworks
- • Mentor junior data scientists on methods
Data Science at L3 — the competency bar
AI in this role
- • Modeling statistical approaches
- • Reviewing analysis code
- • Creating methodology docs
L4 — Staff Data Scientist / Manager, Data Science Transformational
Mission
Lead data science projects and mentor others
This tour of duty
Build a model that becomes production infrastructure
Own the outcomes
- • Lead data science projects across domains
- • Design analytical approaches for complex problems
- • Establish statistical rigor and best practices
- • Mentor data scientists on methodology
- • Influence analytics technology decisions
- • Build relationships with business leaders
Data Science at L4 — the competency bar
AI in this role
- • Designing analytical frameworks
- • Analyzing model performance
- • Generating technical specs
L5 — Senior Staff Data Scientist / Senior Manager, Data Science Transformational
Mission
Drive data science practices across the org
This tour of duty
Drive analytical practices that improve the org
Own the outcomes
- • Define data science strategy and practices
- • Set standards for analytical methodology
- • Lead complex analytical initiatives
- • Drive adoption of rigorous practices
- • Shape data science practices org-wide
- • Represent data science in technical planning
Data Science at L5 — the competency bar
AI in this role
- • Evaluating statistical methods
- • Building methodology docs
- • Creating roadmaps
L6 — Director, Data Science Transformational
Mission
Set data science direction company-wide
This tour of duty
Define data science standards that elevate rigor
Own the outcomes
- • Define analytical vision and roadmap
- • Architect statistical methodologies and frameworks that scale
- • Solve the most complex analytical and modeling challenges
- • Own analytical impact and quality metrics
- • Drive organizational alignment on analytics
- • Represent data science in executive discussions
Data Science at L6 — the competency bar
AI in this role
- • Analyzing patterns across analyses
- • Generating standards
- • Building knowledge bases
L7 — Senior Director, Data Science Transformational
Mission
Shape the company's data science vision
This tour of duty
Solve an analytical problem that unlocks new value
Own the outcomes
- • Set data science strategy company-wide
- • Align data science with business goals
- • Build hiring and development for data science
- • Define industry-leading analytical capabilities
- • Shape company-wide analytics operating model
- • Lead data science planning and investment
Data Science at L7 — the competency bar
AI in this role
- • Modeling analytical evolution
- • Analyzing research trends
- • Creating vision documents
L8 — VP, Data Science Foundational
Mission
Build and lead data science teams
This tour of duty
Build a data science team that delivers insights
Own the outcomes
- • Own data science outcomes at scale
- • Drive transformation for analytical excellence
- • Build systems for analytical rigor and impact
- • Shape company strategy through data science lens
- • Establish operating model for data science org
- • Lead cross-functional analytics alignment
Data Science at L8 — the competency bar
AI in this role
- • Building DS dashboards
- • Analyzing team patterns
- • Creating hiring frameworks
L9 — SVP of Data Science Foundational
Mission
Own data science strategy and execution
This tour of duty
Transform data science practices across the org
Own the outcomes
- • Shape company data science vision
- • Represent data science at executive level
- • Define multi-year analytical capability roadmap
- • Build research partnerships and relationships
- • Lead data science through evolution
- • Establish data science as company differentiator
Data Science at L9 — the competency bar
AI in this role
- • Modeling DS scenarios
- • Building strategy documents
- • Designing knowledge infrastructure
What Hiring Managers Look For
L1-L3: You ship production models that actually move business metrics, not just notebooks that impress other data scientists.
L4-L6: You architect data products that scale across teams and can articulate why your technical choices directly impact company strategy.
L7+: You demonstrate how data infrastructure investments compound into sustainable competitive advantages, with specific examples of organizational transformation you've led.
Common Career Transitions
Data Science → Product at L4-L5 for customer-facing analytics ownership
Data Science → Engineering Leadership at L5-L6 for broader technical architecture scope
Data Science → Strategy/Operations at L6+ for business-critical decision making authority
Official Classifications
| System | Code | Official Title |
|---|---|---|
| O*NET-SOC (US) | 15-2051.00 | Data Scientists |
| ISCO-08 (UN/ILO) | 2120 | Mathematicians, actuaries and statisticians |
| ESCO (EU) | — | statistician |
| SSOC 2024 (Singapore) | 21200 | Mathematicians, Actuaries and Statisticians |
| NCO-2015 (India) | 2120.0100 | Mathematician, Actuary and Statistician |
At L6 and above, the manager classification 1330 — Information and communications technology service managers applies IN ADDITION to the professional code — a manager is a superset of the individual contributor, never a replacement.
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