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Data Scientist Machine Learning Jobs in Nevada (NOW HIRING)

Pearson

Specialist, Data Scientist

Carson City, NV · On-site

$125 - $140/hr

IC20 -- Data Scientist (VALUE) Level intent: Independently delivers data science solutions ... You will analyze structured and unstructured data, develop statistical and machine learning ...

Pwc

AI & GenAI Data Scientist - Manager

Las Vegas, NV · On-site

$99K - $232K/yr

Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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All JobsData Scientist Machine Learning JobsData Scientist Machine Learning Jobs in Nevada

Data Scientist Machine Learning information

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How much do data scientist machine learning jobs pay per year?

As of Sep 6, 2026, the average yearly pay for data scientist machine learning in Nevada is $124,985.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,300.00 and $138,500.00 per year, depending on experience, location, and employer.

What is a data scientist machine learning?

A Data Scientist specializing in Machine Learning (ML) uses statistical methods, algorithms, and computational power to analyze data and create predictive models. They work with large datasets to identify patterns, train machine learning models, and improve decision-making processes. Responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They may collaborate with engineers and business teams to deploy models in real-world applications. Strong skills in programming (Python, R), ML frameworks (TensorFlow, Scikit-learn), and data visualization are essential.

What are the typical day-to-day responsibilities of a data scientist machine learning?

On a typical day, a Data Scientist specializing in Machine Learning might gather and preprocess data, design and implement machine learning models, and evaluate their performance to solve real-world problems. They often collaborate with data engineers, software developers, and business stakeholders to translate business objectives into technical solutions and integrate models into existing systems. Other responsibilities can include visualizing data insights, conducting experiments to tune algorithms, and staying current with new developments in the field. The work is highly collaborative and iterative, requiring clear communication with various teams to ensure project goals are met efficiently.

What are the key skills and qualifications needed to thrive in the data scientist machine learning position, and why are they important?

To excel as a Data Scientist Machine Learning, you need a strong proficiency in statistics, programming (typically Python or R), and a solid understanding of machine learning algorithms, usually backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications in data science or machine learning, is commonly expected. Analytical thinking, problem-solving skills, and effective communication are vital soft skills in this profession. These qualifications combine to drive impactful insights and enable the successful development and deployment of machine learning models in business environments.

What is the salary of data scientist in machine learning?

The salary of a data scientist specializing in machine learning typically ranges from $90,000 to $150,000 annually, depending on experience, location, and industry. Senior roles or those with advanced skills in programming, statistical analysis, and tools like Python or TensorFlow may earn higher compensation.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Nevada?

The most popular types of Data Scientist Machine Learning jobs in Nevada are:

What are popular job titles related to Data Scientist Machine Learning jobs in Nevada?

For Data Scientist Machine Learning jobs in Nevada, the most frequently searched job titles are:

What job categories do people searching Data Scientist Machine Learning jobs in Nevada look for?

The top searched job categories for Data Scientist Machine Learning jobs in Nevada are:

Data Scientist Machine Learning jobs near you
Infographic showing various Data Scientist Machine Learning job openings in Nevada as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $124,985 per year, or $60.1 per hour.

Senior Data Scientist or Machine learning Consultant

3B Staffing LLC

Las Vegas, NV • Remote

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Title Senior Data Scientist or Machine learning Consultant
Location: Remote but Las Vegas, NV

Job Description:

Data Analysis: Collect, clean, and preprocess data from various sources to build datasets for analysis.
Machine Learning: Develop and implement machine learning algorithms and models for predictive and prescriptive analytics.
Statistical Analysis: Apply statistical techniques to interpret data, test hypotheses, and identify trends.
Data Visualization: Create compelling data visualizations and reports to communicate findings to non-technical stakeholders.
Feature Engineering: Identify and engineer relevant features for machine learning models.
Model Evaluation: Evaluate model performance, make improvements, and iterate on model development.
Data Insights: Provide actionable insights and recommendations based on data analysis.
Collaboration: Collaborate with cross-functional teams to integrate data-driven solutions into business processes."

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