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Data Engineer Data Scientist Jobs in Arizona (NOW HIRING)

Data Scientist

Phoenix, AZ ยท On-site

$136K - $164K/yr

Data Scientist III (Generative AI) Location: Phoenix, AZ Start Date: ASAP Duration: Permanent ... Design, validate, and evaluate solutions using Python, SQL, and other programming tools * Transform ...

We achieve this through deep vertical integration, with design, engineering, and production ... About the Role As a Senior Data Scientist, Field Quality , you will report to a Field Quality ...

Data Scientist Position Summary We are seeking a Data Scientist to partner with business ... Optimization techniques (linear and dynamic programming) * Handsโ€‘on experience with: * Python

We achieve this through deep vertical integration, with design, engineering, and production ... About the Role As a Senior Data Scientist, Field Quality , you will report to a Field Quality ...

We achieve this through deep vertical integration, with design, engineering, and production ... About the Role As a Senior Data Scientist, Field Quality , you will report to a Field Quality ...

Experience with at least one statistical programming language: SAS, R, and/or Python Other Things ... data science, quantitative marketing, operations research, industrial engineering, etc.

Data Scientist

Phoenix, AZ ยท On-site +1

... and software engineering. Identifies patterns and looks for opportunities for optimization ... Basic data science concepts: probability, statistics, hypothesis testing, machine learning, natural ...

Senior Data Engineer / Data Curator

Phoenix, AZ ยท On-site

$130K - $177K/yr

Bachelor's degree in Computer Science, Data Science, or a related field. Technical Skills: * 5+ years of experience in data engineering, data wrangling, or data curation, particularly in machine ...

Senior Data Engineer / Data Curator

Phoenix, AZ ยท On-site

$130K - $177K/yr

Bachelor's degree in Computer Science, Data Science, or a related field. Technical Skills: * 5+ years of experience in data engineering, data wrangling, or data curation, particularly in machine ...

In the role of Data Scientist I, we'll count on you to: Handle highly sensitive and confidential ... Proficiency with data engineering tools and languages such as SQL, Power Query, and Pandas

In the role of Data Scientist I, we'll count on you to: Handle highly sensitive and confidential ... Proficiency with data engineering tools and languages such as SQL, Power Query, and Pandas

Data Scientist / Senior Data Scientist We are seeking a highly skilled Data Scientist with strong experience in Generative AI, traditional Machine Learning, and ML Ops. The ideal candidate will have ...

Data Scientist / Senior Data Scientist We are seeking a highly skilled Data Scientist with strong experience in Generative AI, traditional Machine Learning, and ML Ops. The ideal candidate will have ...

Performs hypothesis testing, prompt engineering, time series analysis, and experimentation ... data science workflows. * Experience with common commercial analytics topics (e.g. pricing ...

Performs hypothesis testing, prompt engineering, time series analysis, and experimentation ... data science workflows. * Experience with common commercial analytics topics (e.g. pricing ...

Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field. Master's degree is a plus. * Experience * 3- 5 years of experience in data science or ...

Performs hypothesis testing, prompt engineering, time series analysis, and experimentation ... data science workflows. * Experience with common commercial analytics topics (e.g. pricing ...

Showing results 21-40

Data Engineer Data Scientist information

How do data engineer data scientists typically collaborate with other teams within an organization?

Data Engineer Data Scientists often work closely with data analysts, software engineers, and business stakeholders to ensure that data pipelines are both reliable and tailored to business needs. They are responsible for transforming raw data into actionable insights, which means they regularly participate in cross-functional meetings to understand project requirements and feedback. Collaboration often includes designing data models, optimizing queries, and deploying machine learning models, all while ensuring data integrity and security. This collaborative environment not only enhances the quality of data-driven solutions but also provides opportunities for continuous learning and professional growth.

What are the key skills and qualifications needed to thrive as a data engineer or data scientist, and why are they important?

To thrive as a Data Engineer or Data Scientist, you need a strong background in mathematics, statistics, programming (commonly Python or SQL), and data modeling, often supported by a degree in computer science or a related field. Familiarity with big data frameworks (such as Hadoop or Spark), data visualization tools, and cloud platforms (like AWS or Azure), as well as relevant certifications, is highly beneficial. Analytical thinking, problem-solving, and effective communication are crucial soft skills for translating data insights into actionable business recommendations. These skills and qualities enable professionals to efficiently process complex data, drive data-informed decisions, and add value to organizations.

What is the difference between Data Engineer Data Scientist vs Data Analyst?

AspectData EngineerData Analyst
Required CredentialsBachelor's/Master's in CS, Engineering, or related; often certifications in cloud or big data toolsBachelor's in Statistics, Math, or related; sometimes certifications in analytics tools
Work EnvironmentBuilds data pipelines, manages databases, works with big data toolsAnalyzes data, creates reports, visualizations for business insights
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing, finance, retail, consulting

While Data Engineers focus on building and maintaining data infrastructure, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills, but Data Engineers are more involved in data architecture, whereas Data Analysts focus on data analysis and reporting.

Can a data engineer work as a data scientist?

A data engineer can transition to a data scientist role since both require strong skills in data manipulation, programming, and understanding of data systems. However, data scientists typically focus more on statistical analysis, machine learning, and modeling, which may require additional training or experience. Familiarity with tools like Python, R, and SQL is common to both roles.

What cities in Arizona are hiring for Data Engineer Data Scientist jobs?

Cities in Arizona with the most Data Engineer Data Scientist job openings:

Infographic showing various Data Engineer Data Scientist job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Data Scientist

Phoenix, AZ โ€ข On-site

$136K - $164K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 29 days ago


Job description

Job Title: Data Scientist III (Generative AI) Location: Phoenix, AZ Start Date: ASAP Duration: Permanent Compensation: $136,900 - $164,300 Benefits: Eligible for Health, Dental, Vision, 401K, PTO Must be authorized to work in the U.S. This position is not eligible for sponsorship .

Job Description: Our client is seeking a Data Scientist III specializing in Generative AI and agentic architectures, with proven experience building and scaling end-to-end RAG solutions and autonomous AI agents while embedding Responsible AI practices across model development, deployment, and governance. This role drives internal data analytics projects ranging from short data explorations to long-term implementations of advanced predictive and machine learning models. The Data Scientist III communicates technical and analytical concepts across all levels of the organization and influences the roadmap through data-based recommendations. You will join a growing team of data scientists and help shape how AI is built and deployed across the business.

Day-to-Day Responsibilities:

  • Build robust Agentic AI and RAG applications using Responsible AI practices across model development, deployment, and governance
  • Participate in the end-to-end data science project lifecycle - data mining and exploration, model development and evaluation, production deployment, measurement, and tracking
  • Perform time-series analyses, hypothesis testing, and causal analyses to statistically assess impact and extract trends across functional areas
  • Build statistical models to enhance understanding of trends and predict future performance
  • Design experiments and interpret results to draw detailed, actionable conclusions
  • Design, validate, and evaluate solutions using Python, SQL, and other programming tools
  • Transform data into actionable insights and recommendations, and support standard analyses, reports, and dashboards
  • Collaborate with stakeholders and other teams to gather data, build relationships, and champion ML capabilities for non-technical audiences
  • Educate and mentor team members on data science best practices, statistical programming, and data preparation

Minimum Requirements:

  • 5 years of experience as a Data Scientist working with large databases to perform complex analysis
  • Master's degree in a STEM field
  • Strong MLOps background - big focus, including carrying models through the full development cycle into live production
  • Must currently reside within commuting distance to Phoenix, AZ
  • 5 years of Python
  • 5 years of SQL
  • Git experience
  • Experience building and deploying ML/AI models in a cloud computing environment (e.g., AWS) and/or enterprise IT environment
  • Strong statistical modeling skills (multivariate regression, logistic regression, cluster analysis, design of experiments, decision trees)
  • Strong communication skills and experience working directly with stakeholders
  • Hands-on AI experience (at minimum, active personal use)
Preferred Qualifications:
  • Snowflake
  • AWS
  • Startup mindset
  • Proficiency with Excel and PowerBI
  • Experience with commercial analytics (pricing optimization, customer segmentation, churn/LTV) or operational analytics (logistics, route optimization, maintenance optimization)