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Data Science Research Assistant Jobs in Arizona (NOW HIRING)

State Farm

Data Scientist

Tempe, AZ · Hybrid

Instill a business-oriented mindset that drives the data science & research agenda * Build and maintain a relationship with the open-source community by creating and contributing to open-source data ...

Exponent Inc.

Research Assistant

Phoenix, AZ · On-site

$18.25 - $25/hr

D.s * 90+ Disciplines * 30+ Offices globally Our Opportunity We are currently seeking Research Assistants for our Data Sciences Practice in Phoenix, AZ . In this role, you will work as part of a team ...

Exponent Inc.

Research Assistant

Phoenix, AZ

$18.25 - $25/hr

D.s * 90+ Disciplines * 30+ Offices globally We are currently seeking Research Assistants for our Data Sciences Practice in Phoenix, AZ . In this role, you will work as part of a team to conduct and ...

Exponent Inc.

Research Assistant

Phoenix, AZ

$18.25 - $25/hr

D.s * 90+ Disciplines * 30+ Offices globally Our Opportunity We are currently seeking Research Assistants for our Data Sciences Practice in Phoenix, AZ . In this role, you will work as part of a team ...

ATSU PUBLIC

ASDOH - Student Research Assistant

Mesa, AZ · On-site

$13.50 - $17/hr

Activities could extend to saliva sample collection, sample storage, and data entry. This position ... sciences, dentistry, public health, or biomedical research. This position also supports ...

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How much do data science research assistant jobs pay per hour?

As of Jul 26, 2026, the average hourly pay for data science research assistant in Arizona is $20.42, according to ZipRecruiter salary data. Most workers in this role earn between $17.26 and $23.75 per hour, depending on experience, location, and employer.

Is 30 too late for data science?

Data science research assistants can enter the field at any age, including 30, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is less important than demonstrated competence and ongoing development in the field.

What does a Data Science Research Assistant do?

A Data Science Research Assistant supports research projects by gathering, cleaning, and analyzing data using statistical and computational techniques. They assist senior researchers with designing experiments, developing models, and interpreting results. Typical tasks include data preprocessing, coding in languages like Python or R, literature reviews, and creating visualizations to summarize findings. Their work helps advance scientific knowledge and inform decision-making based on data-driven insights.

What is the difference between Data Science Research Assistant vs Data Analyst?

AspectData Science Research AssistantData Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fieldBachelor's or higher in Data Analysis, Statistics, or related field
Work EnvironmentResearch labs, academic institutions, or research-focused organizationsBusiness settings, corporate offices, or consulting firms
Employer & Industry UsageUniversities, research institutes, government agenciesCorporations, marketing firms, finance, healthcare
Common Search & ComparisonYesNo

Data Science Research Assistants typically focus on supporting research projects through data collection, analysis, and modeling in academic or research settings. Data Analysts primarily interpret data to help organizations make business decisions. While both roles require strong analytical skills and knowledge of data tools, the research assistant role emphasizes academic research and experimentation, whereas data analysts focus on business insights and reporting.

What are the key skills and qualifications needed to thrive as a Data Science Research Assistant, and why are they important?

To thrive as a Data Science Research Assistant, you need a strong background in statistics, machine learning, and programming (often with a degree in computer science, statistics, or related fields). Familiarity with tools such as Python, R, Jupyter Notebooks, and data visualization libraries as well as experience with version control systems like Git is typical, and coursework or certifications in data science can be beneficial. Attention to detail, problem-solving ability, and strong communication skills are essential to effectively analyze data, interpret results, and collaborate with research teams. These skills and qualities are critical for producing reliable insights, supporting research objectives, and ensuring the integrity of data-driven projects.

Is AI replacing data scientists?

AI is transforming the role of data science research assistants by automating routine tasks like data cleaning and analysis, but it does not replace the need for human expertise in designing models, interpreting results, and making strategic decisions. Data scientists and research assistants with skills in programming, statistical analysis, and machine learning remain essential for developing and deploying AI solutions effectively. AI tools serve as complements that enhance productivity rather than substitutes for skilled data professionals.

Is 40 too late for data science?

Age is not a barrier to becoming a data science research assistant; many professionals transition into data science later in their careers. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be developed through online courses, certifications, and practical experience regardless of age.

How do Data Science Research Assistants typically collaborate with other team members during a research project?

Data Science Research Assistants frequently work alongside data scientists, research leads, and subject matter experts to support ongoing research. Their responsibilities often include cleaning and preprocessing data, performing exploratory analyses, and implementing models. Regular collaboration occurs through team meetings, code reviews, and sharing findings, ensuring alignment with project goals. Open communication and adaptability are essential, as priorities and datasets can shift based on project needs.

What is a data research assistant?

A data research assistant is a professional who supports data collection, analysis, and interpretation for research projects. They often work with statistical tools and programming languages like Python or R and may assist in preparing reports or visualizations under the guidance of senior researchers.
What are popular job titles related to Data Science Research Assistant jobs in Arizona? For Data Science Research Assistant jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Data Science Research Assistant jobs in Arizona look for? The top searched job categories for Data Science Research Assistant jobs in Arizona are:
What cities in Arizona are hiring for Data Science Research Assistant jobs? Cities in Arizona with the most Data Science Research Assistant job openings:
Data Science Research Assistant jobs near you
Infographic showing various Data Science Research Assistant job openings in Arizona as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 17% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $42,467 per year, or $20.4 per hour.
Scientific Analyst II

Scientific Analyst II

University of Arizona

Tucson, AZ

Other

Posted 7 days ago

University Of Arizona rating

7.2

Company rating: 7.2 out of 10

Based on 67 frontline employees who took The Breakroom Quiz

383rd of 611 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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