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Entry Level Data Analyst Fresh Graduate Jobs in Tucson, AZ

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

Data Analysis and Machine Learning Pipeline Development: * Under moderate guidance collaborate in ... graduate students on data science methods and tools as needed. * Stay current with literature in ...

Department of Justice

Investigative Analyst

Tucson, AZ · On-site

$52K/yr

You will be responsible for collecting and analyzing a variety of intelligence data, and processing ... Graduate level education must demonstrate the competencies necessary to do the work of the position ...

Varsity Tutors

Data Science Tutor

Tucson, AZ · Remote

$18 - $40/hr

Ability to explain probability distributions, regression analysis, A/B testing, and data pipeline design while preparing students for data science roles, analytics careers, and graduate programs.

Cornerstone Research

Analyst

Tucson, AZ · On-site

$112K/yr

In addition, top-performing Analysts are eligible for graduate school financial support and an ... We prefer students who have completed at least one internship in business, economics, data analysis ...

Cornerstone Research

Analyst

Tucson, AZ

$112K/yr

In addition, top-performing Analysts are eligible for graduate school financial support and an ... We prefer students who have completed at least one internship in business, economics, data analysis ...

Office of the Inspector General

Program Analyst

Tucson, AZ · On-site +1

$51K - $100K/yr

OR Applicants may have completed one full year of graduate level education or may have a bachelor ... data/file reviews; * assisting in performing quantitative and qualitative analysis on program ...

Office of the Inspector General

Program Analyst

Tucson, AZ · On-site +1

$51K - $100K/yr

OR Applicants may have completed one full year of graduate level education or may have a bachelor ... data/file reviews; * assisting in performing quantitative and qualitative analysis on program ...

UNIVERSITY OF ARIZONA

IT Business Analyst I

Tucson, AZ · On-site

$59 - $74/hr

This entry-level role helps gather, document, and organize business and functional requirements for ... Perform data validation activities related to IT assets, inventory, and purchasing records.

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Entry Level Data Analyst Fresh Graduate information

See Tucson, AZ salary details

$12

$31

$58

How much do entry level data analyst fresh graduate jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for entry level data analyst fresh graduate in Tucson, AZ is $31.13, according to ZipRecruiter salary data. Most workers in this role earn between $20.00 and $34.76 per hour, depending on experience, location, and employer.

What is the difference between Entry Level Data Analyst Fresh Graduate vs Data Technician?

AspectEntry Level Data Analyst Fresh GraduateData Technician
Required CredentialsBachelor's in Data Science, Statistics, or related fieldTechnical diploma or associate degree in IT or data management
Work EnvironmentOffice setting, collaborative teams, data analysis projectsData centers, IT departments, technical support environments
Employer & Industry UsageBusiness, finance, marketing, healthcareIT firms, data management companies, tech support
Common Search & ComparisonYesYes

The Entry Level Data Analyst Fresh Graduate typically focuses on analyzing data, creating reports, and supporting decision-making processes using statistical tools. In contrast, a Data Technician primarily manages data systems, maintains databases, and ensures data integrity. While both roles require technical skills, the analyst role emphasizes interpretation and insights, whereas the technician role centers on data infrastructure and technical support.

What are the most commonly searched types of Data Analyst Fresh Graduate jobs in Tucson, AZ?

The most popular types of Data Analyst Fresh Graduate jobs in Tucson, AZ are:

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For Entry Level Data Analyst Fresh Graduate jobs in Tucson, AZ, the most frequently searched job titles are:

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Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Full-time

Re-posted 17 days ago

University Of Arizona rating

7.4

Company rating: 7.4 out of 10

Based on 69 frontline employees who took The Breakroom Quiz

338th of 630 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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