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Freelance Data Science Research Assistant Jobs in Arizona

Globe Telecom, Inc.

Data Science Manager

Globe, AZ

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 ...

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 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 ...

Shamrock

Data Science Analyst

Phoenix, AZ · On-site

Master's degree in Data Science, Analytics, Statistics, Operations Research, Computer Science, Industrial Engineering, or related discipline preferred. * 2-5 years of experience in data science ...

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 ...

ALLIANCE CLINICAL LLC

Research Assistant

Phoenix, AZ · On-site

$19 - $26.25/hr

This role is responsible for collecting and documenting clinical data, obtaining informed consent ... The Research Assistant ensures compliance with study protocols, regulatory requirements, and ...

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Freelance Data Science Research Assistant information

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

AspectFreelance Data Science Research AssistantData Analyst
CredentialsTypically requires a degree in data science, statistics, or related fields; certifications are a plusUsually requires a degree in statistics, mathematics, or related fields; certifications like Microsoft or Tableau are common
Work EnvironmentRemote or freelance projects, often short-term or contract-basedOften employed full-time in organizations or agencies, but also freelance roles exist
Industry UsageUsed in research projects, academia, or specialized consultingCommon in business, marketing, finance, and healthcare sectors

The main difference is that a Freelance Data Science Research Assistant focuses on supporting research projects with advanced data analysis, often on a freelance basis, while a Data Analyst typically works within organizations analyzing data to inform business decisions. Both roles require strong analytical skills and relevant credentials, but their work settings and project types differ.

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Freelance Data Science Research Assistant jobs near you
Infographic showing various Freelance Data Science Research Assistant 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 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Scientific Analyst II

University of Arizona

Tucson, AZ

Full-time

Re-posted 29 days ago

University Of Arizona rating

7.3

Company rating: 7.3 out of 10

Based on 68 frontline employees who took The Breakroom Quiz

362nd of 618 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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