Data Science — P4

DATASC.DATASCIE8990.P4

P4P4 — Senior Professionalhigh0.80approvedglobalv1

Focuses on extracting insights and building predictive systems from structured and unstructured data using statistical analysis, machine learning, and modeling. Distinct from Data Engineering (which builds the data pipelines and infrastructure) and Data/Business Analytics (which centers on descriptive reporting and BI dashboards), Data Science centers on designing, training, and deploying models, running rigorous analyses, and translating ambiguous business questions into quantitative solutions.

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Level

P4 · P4 — Senior Professional · 5–8 yrs

Function · Focus

Data Science & Analytics · Data Science

Market pay (median)

$109k ($86k–$139k)

Pay basis

function-anchored · wide bars

Focus — Data Science

Material SKILL differential vs the function baseline.

Responsibilities by level

What this person actually does at each level on the professional track — escalating scope, not one generic blob. Your level is highlighted.

Works on clear, well-defined tasks within analyses or models designed by a manager or senior data scientist, under close supervision.
Writes SQL queries to calculate defined metrics such as customer churn rates and creates dashboards (e.g., purchases across marketing channels).
Develops simple predictive models and runs analyses with step-by-step guidance.
Learns organizational tools, data sources, and processes; gets to know the data well enough to deliver against bounded tasks.
Plans work a few weeks out and presents findings to peers and their manager.

Owns whole problems end-to-end rather than isolated tasks, taking overall direction from senior data scientists.
Designs and leads an analysis or model to completion with minimal manager input on familiar problem types.
Writes code to collect, clean, and analyze data, and constructs the ETL pipeline that provides training data for machine learning models.
Handles most of the technical work for a project independently, applying judgment in familiar modeling contexts.
Manages moderately ambiguous problems with wider scope and chooses appropriate methods from conventional approaches.

Understands a business problem and independently designs and leads an entire analysis or model from framing to delivery.
Handles ambiguous problems with wider scopes, exercising day-to-day autonomy over how projects are run with milestone review.
Constructs machine learning models and the full pipeline of data collection, cleaning, feature engineering, and validation.
Networks with senior professionals and stakeholders to scope diverse problems and translate findings into recommendations.
May coordinate the activities of junior data scientists contributing to a shared project.

P4this profile

Leads complex analyses and modeling efforts that have impact across a function, selecting methods and evaluating in-depth across many variables.
Identifies opportunities for new data science applications that drive measurable business value and prioritizes them with minimal managerial support.
Drives opportunities through to production utilization, coordinating across engineering, product, and analytics groups.
Mentors junior and mid-level data scientists on technical problems and project approach; may lead a project team.
Demonstrates high ownership, managing crises and complex deliverables with broad latitude over technical approach.

Acts as a driving force within the analytics organization and broader business, contributing to company objectives through strategic data science work.
Consistently identifies and frames high-value data science opportunities on broad or special assignments and brings them to utilization.
Works alongside C-suite executives, bridging technical, analytical, and business considerations as an internal and external spokesperson.
Coaches and mentors data scientists across levels on project problems and career direction; may supervise others on special tasks.
Sets the bar for technical excellence and resolves intangible, high-independence problems where standard answers do not exist.

Defines which projects should exist and figures out which questions the business should be asking; serves as strategic mind and high-level technical architect.
Provides technical leadership through influence and mentorship rather than people management, shaping the organization's data science capability.
Influences architectural direction and weighs trade-offs in platform design and ML deployment across the organization.
Guides senior leadership on the long-term direction of AI/ML capabilities, aligning technical decisions with product vision, company goals, and regulatory constraints.
Assesses the risk of AI systems and research investments, operating at the intersection of deep technical depth and business strategy.

Sets direction for the data science function and anticipates emerging challenges, defining long-term AI/ML roadmaps that influence company-wide strategy and industry practice.
Solves precedent-free, ambiguous problems and develops new models, methods, or technologies with broad business and industry consequences.
Networks with executives, boards, regulators, and industry leaders, persuading and educating senior stakeholders on strategic data and AI priorities.
Provides high-level mentorship to senior and principal data scientists, shaping company-wide technical capability without direct reports.
Operates with complete independence, establishing the governance, ethics, and risk frameworks that define how AI systems are built and deployed.

Level guidelines

The universal leveling rubric applied to this function — how scope, complexity, collaboration, and experience step up across levels.

Level	Knowledge & Application	Complexity & Problem Solving	Collaboration & Interaction	Typical Degree & Years
P1	Applies foundational statistics, Python/SQL, and basic modeling to well-defined tasks; knows the data and standard tools well enough to deliver bounded analyses.	Solves routine problems with standard answers within models and analyses designed by others.	Maintains stable working relationships with peers and manager; presents findings internally to the immediate team.	0–1 years; new graduate or intern with a quantitative degree.
P2	Applies machine learning, data wrangling, and ETL construction across whole problems in familiar domains; selects from conventional methods.	Exercises judgment on moderately ambiguous problems, designing and completing an analysis or model with minimal direction.	Builds productive project relationships and takes overall direction from senior data scientists; may informally guide entry-level staff.	2+ years with a BA, or an MS/PhD with no prior experience.
P3	Independently applies the full modeling lifecycle — framing, feature engineering, model construction, validation — to diverse business problems.	Evaluates identifiable factors across ambiguous, wider-scope problems and selects approaches with day-to-day independence.	Networks with senior professionals and stakeholders; may coordinate the project activities of junior data scientists.	5+ years (BA), 3+ years (MA), or PhD without experience.
P4	Brings in-depth expertise across machine learning, deep learning, and cloud deployment to complex problems with functional impact.	Performs in-depth analysis of complex variables, selecting methods and driving opportunities from idea to production.	Coordinates across engineering, product, and analytics groups; influences decisions and mentors junior and mid-level staff.	8+ years, often with graduate education in a quantitative field.
P5	Applies extensive expertise to strategic, intangible problems contributing directly to company objectives; recognized internal authority.	Resolves problems with no standard answers, acting with high independence on broad and special assignments.	Works directly with C-suite executives and builds influential internal/external networks as a spokesperson; coaches across levels.	12+ years with extensive data science and modeling expertise.
P6	Shapes the organization's data science and AI/ML direction with visionary, field-influencing technical judgment and architectural authority.	Solves field-shaping problems — defining which questions matter, weighing platform and deployment trade-offs, and assessing AI risk.	Influences industry and company as a recognized thought leader; leads through influence and high-level mentorship, not people management.	15+ years; principal-level expert, often PhD with industry leadership.
P7	Develops new theories, models, and technologies that advance the field and define long-term company and industry data/AI roadmaps.	Solves precedent-free, ambiguous problems with broad business and industry consequences; anticipates emerging challenges.	Networks with executives, boards, regulators, and industry leaders; persuades and educates on strategic priorities and mentors senior professionals.	20+ years, or equivalent recognition (often PhD plus significant industry contributions, patents, or publications).

Skills

Focus-specific skills the role applies — the relevance layer beyond the occupational base.

Machine Learning: Applies algorithms to extract and analyze information from large structured and unstructured datasets to build predictive models.
Python: Uses this data-oriented programming language effectively during the delivery of day-to-day data science tasks.
SQL: Writes queries to retrieve and join data from relational databases, critical for data retrieval and metric calculation.
R: Uses this statistical programming language for analysis and modeling during day-to-day tasks.
Natural Language Processing: Applies techniques to process and analyze human language data within models and analyses.
Statistics and Mathematics: Applies probability, linear algebra, and calculus to underpin accurate analysis and reliable model building.
Data Mining: Extracts patterns and insights from large datasets to inform models and business decisions.
Data Wrangling: Identifies and fixes data quality issues, handles missing values, and transforms data into a consistent structure.
Data Visualization: Visualizes, interprets, and reports data findings using visualization software.
Feature Selection: Applies feature selection algorithms to improve the accuracy and efficiency of predictive models.
Cloud Computing: Applies expertise in cloud platforms for data storage, processing, and model deployment.
Deep Learning: Builds neural network models using frameworks such as PyTorch and TensorFlow.

Provenance

The evidence base behind this profile — every layer is sourced; quality is scored by an adversarial review panel (1–5; passes at ≥4 on the minimum dimension).

Level differentiation4.5Focus specificity5.0Concreteness4.5Factual accuracy4.5Real-world coverage4.0

11 sources

Level — P4 — Senior Professional

Seasoned professional; handles complex tasks, may lead small teams or projects

Scope: A system or set of related features
Autonomy: Self-directed; reviewed at critical decision points
Complexity: Complex, ambiguous problems; devises new approaches
Impact: Multi-team / function outcomes
Decision rights: Owns technical decisions for a system; influences adjacent design
Leadership: Technical lead for focused efforts; mentors several
Typical experience: 5–8 yrs

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