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Google Data Science Jobs in Arizona (NOW HIRING)

Zenotis Technologies INC

Data Scientist

Phoenix, AZ · On-site

This role requires someone who can work across the complete data science lifecycle--from ... Experience with Google Cloud Platform , modern data pipelines, and LLMs/Agentic AI will be a ...

Deloitte

Agentic AI, AI & Data Science Engineer

Tempe, AZ · On-site

$109K - $131K/yr

Work you'll do As an AI and Data Science Engineer III on the AI & Data team, you will be ... Gemini API and Google AI Studio; BigQuery (for data processing and analytics); Cloud Run, Cloud ...

MUFG

AVP, Audit Execution Analytics

Tempe, AZ · Hybrid

$106K - $130K/yr

Professional certifications related to data analytics, data science and/or audit are preferred, such as Google Data Analytics Professional Certificate, IBM Data Analyst Professional Certificate ...

Launch Potato

Lead Data Scientist, AdTech

Phoenix, AZ · Hybrid

$175K - $200K/yr

YOUR ROLE Own the full data science engine for a priority vertical, from business problem to ... Modeling against ad-platform data points (Google, Meta, native) * LLMs / deep learning applied to ...

Insight

S. in Computer Science, Data Science, MBA, or equivalent practical experience in the field. What ... Becoming a Google Cloud Certified Professional Data Engineer or Machine Learning Engineer (if not ...

Deloitte

Databricks Data Engineer II

Tempe, AZ · On-site

$109K - $131K/yr

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Mathematics ... Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) * Ability to travel 50 ...

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Google Data Science information

Can a Data Scientist work in Google?

Yes, Data Scientists can work at Google, where they analyze large datasets, develop machine learning models, and use tools like Python and TensorFlow. Google typically requires relevant experience, strong analytical skills, and a background in computer science or related fields for data science roles.

How much do data scientists make at Google?

Data scientists at Google typically earn a median salary ranging from $120,000 to $160,000 per year, depending on experience, location, and level. Total compensation often includes bonuses, stock options, and other benefits, reflecting the company's competitive pay structure for technical roles requiring skills in machine learning, programming, and data analysis.

What are the key skills and qualifications needed to thrive in the Google Data Science position, and why are they important?

To thrive as a Google Data Science professional, you need a strong foundation in statistical analysis, machine learning, and data manipulation, often supported by a degree in a quantitative field such as computer science, statistics, or mathematics. Proficiency in programming languages like Python or R, experience with large-scale data processing tools (such as SQL, TensorFlow, or BigQuery), and familiarity with cloud-based platforms are commonly required. Excellent problem-solving, communication, and collaboration skills help set candidates apart in effectively translating complex data insights to varied stakeholders. These capabilities are crucial for driving impactful, data-driven decisions within cross-functional teams at Google.

How much does Google pay a Data Scientist?

Google Data Scientists typically earn a base salary ranging from $120,000 to $180,000 annually, with total compensation often including bonuses and stock options that can increase overall earnings. Compensation varies based on experience, location, and skill level, with advanced skills in machine learning and data analysis highly valued.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist, and many professionals successfully transition into the field at age 40 or later. Success depends on acquiring relevant skills such as programming, statistics, and data analysis, often through online courses or certifications, and building a strong portfolio. Employers value experience and problem-solving ability, making it possible to start a data science career at any age with dedication.

What is a Google Data Science job?

A Google Data Science job involves analyzing large datasets to provide insights and drive data-informed decisions. Data scientists at Google apply statistical modeling, machine learning, and analytical techniques to solve complex problems in products like Search, Ads, YouTube, and Cloud. They work closely with engineers, product managers, and business teams to develop data-driven solutions. Strong coding skills in Python or SQL, experience with big data tools, and a solid foundation in statistics are essential for this role.

What types of projects do Google Data Science professionals typically work on?

Google Data Science professionals engage in a wide variety of impactful projects, such as optimizing algorithms for product recommendations, improving user experiences through data-driven insights, and developing predictive models to inform business strategies. They often work closely with product managers, engineers, and designers to translate complex data findings into actionable solutions. The work environment is highly collaborative and fast-paced, with opportunities to contribute to innovative initiatives across different Google products and services. This dynamic setting allows data scientists to continuously expand their skill sets and take on new challenges, fostering both personal and professional growth.

What are popular job titles related to Google Data Science jobs in Arizona? For Google Data Science jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Google Data Science jobs in Arizona look for? The top searched job categories for Google Data Science jobs in Arizona are:
What cities in Arizona are hiring for Google Data Science jobs? Cities in Arizona with the most Google Data Science job openings:
Google Data Science jobs near you
Infographic showing various Google Data Science job openings in Arizona as of July 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.
Data Scientist

Data Scientist

Zenotis Technologies INC

Phoenix, AZ • On-site

Other

Posted 9 days ago

Job description

We are seeking a highly analytical and hands-on Data Scientist to join a team building data-driven personalization solutions while helping drive the organization''s transition into Generative AI and Agentic AI. This role requires someone who can work across the complete data science lifecycle—from understanding and validating large-scale datasets to building production-ready machine learning solutions and contributing to next-generation AI initiatives.

The ideal candidate has strong expertise in Python, SQL, and PySpark, enjoys solving business problems through data, and can communicate insights effectively to stakeholders. Experience with Google Cloud Platform, modern data pipelines, and LLMs/Agentic AI will be a significant advantage.

Key ResponsibilitiesData Analysis & Business Insights

·                     Analyze large-scale structured and unstructured datasets including customer, transaction, and marketing data.

  • Develop a deep understanding of data to identify trends, anomalies, and business opportunities.
  • Translate analytical findings into actionable recommendations for business stakeholders.
  • Present insights clearly to both technical and non-technical audiences.
Data Engineering & Pipeline Development

·                     Develop and maintain scalable data pipelines on Google Cloud Platform (Google Cloud Platform).

  • Extract, transform, validate, and process data from multiple enterprise data sources.
  • Build reliable, production-grade ETL/ELT workflows using PySpark and SQL.
  • Monitor and optimize data pipelines for performance, scalability, and reliability.
Machine Learning

·                     Build, train, evaluate, and deploy machine learning models.

  • Apply supervised and unsupervised learning techniques to solve business problems.
  • Implement end-to-end machine learning workflows from data preparation through model deployment and monitoring.
  • Continuously improve model performance using appropriate evaluation techniques.
Data Validation & Governance

·                     Design robust data validation frameworks to ensure data quality.

  • Identify and resolve data quality issues and data drift.
  • Ensure compliance with enterprise data governance and privacy standards.
  • Implement automated validation and monitoring processes across data pipelines.
Generative AI & Agentic AI

·                     Contribute to AI initiatives involving Large Language Models (LLMs) and Agentic AI.

  • Assist in designing and implementing AI-powered solutions using prompt engineering techniques.
  • Evaluate opportunities to leverage transformers and modern AI frameworks for business use cases.
  • Collaborate on enterprise AI solutions that integrate traditional machine learning with Generative AI capabilities.

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