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Junior Machine Learning Jobs in Arizona (NOW HIRING)

Caris Life Sciences

Senior Data Scientist

Tempe, AZ · On-site

Design, develop, validate, deploy, and monitor data processing and machine learning pipelines ... Provide technical mentorship and guidance to junior team members, as appropriate. Required ...

Caris Life Sciences

Senior Data Scientist

Tempe, AZ · On-site

Design, develop, validate, deploy, and monitor data processing and machine learning pipelines ... Provide technical mentorship and guidance to junior team members, as appropriate. Required ...

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All JobsMachine Learning JobsJunior Machine Learning JobsJunior Machine Learning Jobs in Arizona

Junior Machine Learning information

See Arizona salary details

$7

$25

$44

How much do junior machine learning jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for junior machine learning in Arizona is $25.12, according to ZipRecruiter salary data. Most workers in this role earn between $15.24 and $30.91 per hour, depending on experience, location, and employer.

What is the difference between Junior Machine Learning vs Data Scientist?

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What does a Junior Machine Learning Engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

What types of projects and tasks can a Junior Machine Learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

What are the key skills and qualifications needed to thrive as a Junior Machine Learning Engineer, and why are they important?

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.
What are the most commonly searched types of Machine Learning jobs in Arizona? The most popular types of Machine Learning jobs in Arizona are:
What are popular job titles related to Junior Machine Learning jobs in Arizona? For Junior Machine Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning jobs in Arizona look for? The top searched job categories for Junior Machine Learning jobs in Arizona are:
Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning job openings in Arizona as of June 2026, with employment types broken down into 1% As Needed, 95% Full Time, 3% Part Time, and 1% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $52,250 per year, or $25.1 per hour.
Scientific Analyst II

Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Other

Posted 20 days ago

University Of Arizona rating

7.0

Company rating: 7.0 out of 10

Based on 65 frontline employees who took The Breakroom Quiz

370th of 535 rated colleges and universities

Job description

Data Analysis and Machine Learning Pipeline Development:

  • Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
  • Apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of menopausal hormone therapy (MHT) on brain health.
  • Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

  • Collaborating in computational drug repurposing analyses to identify existing FDA-approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment. Integrate multi-omics data (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
  • Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

  • Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
  • Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
  • Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
  • Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

  • Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
  • Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
  • Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough documentation of analytical methods to ensure transparency and reproducibility.
  • Participate in lab meetings, journal clubs, and professional development activities.

Research Infrastructure and Continuous Improvement:

  • Maintain and improve lab computational infrastructure, including code repositories (GitHub), analytical workflows, and documentation standards.
  • Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
  • Assist in training junior lab members or graduate students on data science methods and tools as needed.
  • Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

  • Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
  • Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
  • Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Knowledge of drug repurposing methodologies or network pharmacology.
  • Knowledge and familiarity with electronic medical records data analysis.
  • Knowledge and proficiency with SQL and database management.
  • Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
  • Ability to manage multiple concurrent projects and meet deadlines.
  • Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
  • Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

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