Data scientist vs machining engineer

The differences between data scientists and machining engineers can be seen in a few details. Each job has different responsibilities and duties. While it typically takes 2-4 years to become a data scientist, becoming a machining engineer takes usually requires 4-6 years. Additionally, a machining engineer has an average salary of $123,716, which is higher than the $106,104 average annual salary of a data scientist.

The top three skills for a data scientist include python, data science and visualization. The most important skills for a machining engineer are python, java, and tensorflow.

Data scientist vs machining engineer overview

	Data Scientist	Machining Engineer
Yearly salary	$106,104	$123,716
Hourly rate	$51.01	$59.48
Growth rate	16%	2%
Number of jobs	106,973	93,823
Job satisfaction	-	-
Most common degree	Bachelor's Degree, 51%	Bachelor's Degree, 53%
Average age	41	41
Years of experience	4	6

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.

What does a machining engineer do?

A machining engineer specializes in designing and developing new tools and mechanical equipment, even analyzing and improving designs to ensure efficiency. Their responsibilities revolve around overseeing and participating in installing, repairing, and maintaining different systems, coordinating with other engineers, and conducting regular inspections to monitor a machines' quality. It is also essential to address any issues or concerns, performing corrective measures right away. Furthermore, should a machining engineer work for a company, it is necessary to adhere to its policies and regulations.

Data scientist vs machining engineer salary

Data scientists and machining engineers have different pay scales, as shown below.

	Data Scientist	Machining Engineer
Average salary	$106,104	$123,716
Salary range	Between $75,000 And $148,000	Between $83,000 And $182,000
Highest paying City	Richmond, CA	San Francisco, CA
Highest paying state	California	California
Best paying company	The Citadel	Airbnb
Best paying industry	Start-up	Start-up

Differences between data scientist and machining engineer education

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

	Data Scientist	Machining Engineer
Most common degree	Bachelor's Degree, 51%	Bachelor's Degree, 53%
Most common major	Computer Science	Electrical Engineering
Most common college	Columbia University in the City of New York	Massachusetts Institute of Technology

Data scientist vs machining engineer demographics

Here are the differences between data scientists' and machining engineers' demographics:

	Data Scientist	Machining Engineer
Average age	41	41
Gender ratio	Male, 79.6% Female, 20.4%	Male, 93.5% Female, 6.5%
Race ratio	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%	Black or African American, 3.4% Unknown, 4.6% Hispanic or Latino, 9.1% Asian, 14.9% White, 68.0% American Indian and Alaska Native, 0.1%
LGBT Percentage	9%	5%

Differences between data scientist and machining engineer duties and responsibilities

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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Machining engineer example responsibilities.

Lead and manage CNC production line including support as required in engineering, material and quality control.
Manage Jenkins security by providing specific access to authorize developers/testers using project base matrix authorization strategy.
Cross-Connect the cable python to the Mervyns network rack.
Research extended axes to be integrate with the FANUC control system.
Select controller for high positional accuracy for precision pointing of LIDAR.
Used Microsoft SQL server reporting services (SSRS) for data reporting.

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Data scientist vs machining engineer skills

Common data scientist skills

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

Common machining engineer skills

Python, 22%
Java, 20%
TensorFlow, 11%
Spark, 10%
Deep Learning, 10%
C++, 6%

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Updated January 8, 2025

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