Data modeler vs data scientist

The differences between data modelers and data scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a data modeler and a data scientist. Additionally, a data scientist has an average salary of $106,104, which is higher than the $100,495 average annual salary of a data modeler.

The top three skills for a data modeler include ETL, data analysis and data architecture. The most important skills for a data scientist are python, data science, and visualization.

Data modeler vs data scientist overview

	Data Modeler	Data Scientist
Yearly salary	$100,495	$106,104
Hourly rate	$48.31	$51.01
Growth rate	9%	16%
Number of jobs	81,645	106,973
Job satisfaction	-	-
Most common degree	Bachelor's Degree, 70%	Bachelor's Degree, 51%
Average age	46	41
Years of experience	4	4

What does a data modeler do?

A data modeler is responsible for designing and creating network systems and applications for efficient and secured data storage solutions. Data modelers work closely with the data management team to identify business needs and execute data modeling techniques for comprehensive analysis. They also strategize in improving existing data systems, upgrading infrastructure, and configuring information for compatibility with every business unit. A data modeler must have excellent technical skills, as well as a strong command of programming languages to modify and optimize data models for smooth navigation and access.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Data modeler vs data scientist salary

Data modelers and data scientists have different pay scales, as shown below.

	Data Modeler	Data Scientist
Average salary	$100,495	$106,104
Salary range	Between $73,000 And $138,000	Between $75,000 And $148,000
Highest paying City	San Francisco, CA	Richmond, CA
Highest paying state	California	California
Best paying company	Meta	The Citadel
Best paying industry	Pharmaceutical	Start-up

Differences between data modeler and data scientist education

There are a few differences between a data modeler and a data scientist in terms of educational background:

	Data Modeler	Data Scientist
Most common degree	Bachelor's Degree, 70%	Bachelor's Degree, 51%
Most common major	Computer Science	Computer Science
Most common college	-	Columbia University in the City of New York

Data modeler vs data scientist demographics

Here are the differences between data modelers' and data scientists' demographics:

	Data Modeler	Data Scientist
Average age	46	41
Gender ratio	Male, 71.0% Female, 29.0%	Male, 79.6% Female, 20.4%
Race ratio	Black or African American, 6.2% Unknown, 5.1% Hispanic or Latino, 8.1% Asian, 28.6% White, 51.5% American Indian and Alaska Native, 0.5%	Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage	6%	9%

Differences between data modeler and data scientist duties and responsibilities

Data modeler example responsibilities.

Lead efforts to analyze data for source/target mappings, create T-SQL scripts for data processing.
Involve in data governance processes relate to data quality and information management, managing the metadata repository etc.
Accomplish at designing dashboards and data summaries for technical and non-technical audiences and facilitating implementation of business strategies and missions.
Design the data marts in dimensional data modeling using star and snowflake schemas.
Develop data architecture prototypes and data models including ETL staging models, audit control models and traditional data warehouse dimension/fact models.
Work extensively with XML schema generation.

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Data scientist example responsibilities.

Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
Perform cross-validation-test on linear regression model of data using scikit-learn.
Develop python base statistical visualization to provide insights of fuzzy social media data.

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Data modeler vs data scientist skills

Common data modeler skills

ETL, 6%
Data Analysis, 6%
Data Architecture, 6%
Physical Data Models, 5%
Data Warehouse, 5%
Tableau, 5%

Common data scientist skills

Python, 13%
Data Science, 10%
Visualization, 5%
Java, 4%
Hadoop, 4%
Tableau, 3%

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Updated January 8, 2025

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