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Data Science Machine Learning Jobs in Arizona (NOW HIRING)

Globe Telecom, Inc.

Data Science Manager

Globe, AZ

The Data Science Manager leads the end-to-end development of data-driven solutions, from ... Build, test, and deploy machine learning and statistical models that address business needs * Track ...

Dlapiper

Data Scientist

Phoenix, AZ · On-site +1

A specialization in machine-learning, artificial intelligence, cognitive science or data science is preferred. Must be self-driven, curious and creative. * Experience must include creating and using ...

Staffingine LLC

AIML Engineer

Scottsdale, AZ · On-site

Experience implementing and supporting endtoend Machine Learning workflows and patterns * Expert level programming skills in Python and experience with Data Science and ML packages and frameworks

Unitek Learning

People Data Scientist

Tempe, AZ · On-site

... data science, people analytics, workforce analytics, applied research, or a related analytical discipline. * Strong knowledge of statistical modeling, predictive analytics, machine learning ...

Unitek Learning

People Data Scientist

Tempe, AZ · On-site

... data science, people analytics, workforce analytics, applied research, or a related analytical discipline. * Strong knowledge of statistical modeling, predictive analytics, machine learning ...

Unitek Learning

People Data Scientist

Tempe, AZ · On-site

... data science, people analytics, workforce analytics, applied research, or a related analytical discipline. * Strong knowledge of statistical modeling, predictive analytics, machine learning ...

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 ...

New

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 ...

New

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 ...

New

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 ...

New

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Showing results 21-40

All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Arizona

Data Science Machine Learning information

See Arizona salary details

$34.9K

$114.4K

$183.1K

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

As of Sep 3, 2026, the average yearly pay for data science machine learning in Arizona is $114,378.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

What are the key skills and qualifications needed to thrive as a data science machine learning professional?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What are some common challenges faced when deploying machine learning models as a data science machine learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

Is data science machine learning a high paying job?

Data science and machine learning roles are generally high-paying within the tech industry due to the specialized skills required, such as programming, statistical analysis, and experience with tools like Python or TensorFlow. Salaries vary based on experience, location, and company size but tend to be above average compared to many other professions.
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $114,378 per year, or $55 per hour.

Data Science Manager

Globe Telecom, Inc.

Globe, AZ

Full-time

Re-posted 25 days ago

Job description

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description The Data Science Manager leads the end-to-end development of data-driven solutions, from translating business needs into data science projects, to building, deploying, and monitoring predictive models in production. This role ensures models deliver measurable business impact, maintains the reliability of machine learning pipelines, and collaborates closely with business, product, and engineering teams to integrate data science solutions into operational systems.

DUTIES AND RESPONSIBILITIES:

Data Exploration & Feature Engineering

  • Lead data extraction, exploration, cleansing, and transformation of large and complex datasets

  • Design, engineer, and validate features needed for predictive models and advanced analytics

  • Build frameworks and data pipelines that combine telco datasets with digital, social, and external data sources to create a holistic customer view.

Model Development & Performance Management

  • Translate business problems into clear data science approaches and model requirements

  • Build, test, and deploy machine learning and statistical models that address business needs

  • Track performance, accuracy, drift, and business value of deployed models

  • Conduct periodic model tuning and ensure continuous improvement aligned with ROI goals.

Insights, Applications & Business Enablement

  • Translate model outputs into clear insights, actionable recommendations, and campaign or operational strategies

  • Identify new opportunities to apply data science, especially in customer behavior prediction, segmentation, and credit risk scoring.

  • Partner with business teams to embed analytics solutions into decision-making and customer lifecycle programs.

REQUIREMENTS:

Work Experience

  • Minimum of three (3) years' experience in customer analytics domain and/or credit risk assessment and financial services, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured data

  • Proven track record of leading and collaborating on advanced analytics strategic initiatives; Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teams

  • Worked with large, unfiltered data sets or data science research

Level of Knowledge

  • Has Knowledge of both structured and unstructured data

  • Must possess core competencies, deep understanding and relevant experience in

  • Scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL);

  • Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or software

  • Big Data: Experience with Big data tools such as HDFS, Cassandra, Storm

  • Database knowledge: skilled in structured database

  • Familiar with most of the following disciplines:

  • Conceptual modeling: to be able to share and articulate modeling;

  • Predictive modeling: most of the big data problems are towards being able to predict future outcomes;

  • Hypothesis testing: being able to develop hypothesis and test them with careful experiments;

  • Natural Language Processing: the interactions between computer and humans;

  • Machine learning: using computers to improve as well as develop algorithms;

  • Statistical analysis: to understand and work around possible limitations in models.

Education

  • Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or Informatics

Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here

Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.

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