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Clinical Statistical Programmer Jobs in Arizona (NOW HIRING)

Lifekind Health

Data Scientist

Scottsdale, AZ · On-site

$80K - $120K/yr

... clinical notes, billing, scheduling) is a strong advantage * Familiarity with HIPAA, PHI handling, and healthcare data governance * Strong understanding of feature engineering, statistical methods ...

CYNET SYSTEMS

Cost Engineer

Chandler, AZ · On-site

$59 - $77/hr

Prepares cost studies using historical data, statistical analysis, and cost and quantity ... We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and ...

Erbe USA Incorporated

Sr. R&D Engineer

Tempe, AZ · On-site

$101K - $139K/yr

Translate customer, clinical, regulatory, and manufacturing requirements into clear product ... Apply statistical methods, risk-based approaches, and objective evidence to support product ...

W. L. Gore & Associates, Inc.

Quality Engineer

Phoenix, AZ · On-site

$73K - $94K/yr

... clinical environments. This role requires the ability to independently manage work, apply risk ... Demonstrated ability to analyze technical data and apply statistical methods to characterize ...

W. L. Gore & Associates, Inc.

Quality Engineer

Flagstaff, AZ · On-site

$65K - $84K/yr

... clinical environments. This role requires the ability to independently manage work, apply risk ... Demonstrated ability to analyze technical data and apply statistical methods to characterize ...

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Clinical Statistical Programmer information

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How much do clinical statistical programmer jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for clinical statistical programmer in Arizona is $50.49, according to ZipRecruiter salary data. Most workers in this role earn between $32.02 and $75.96 per hour, depending on experience, location, and employer.

What are some common challenges faced by clinical statistical programmers when working with clinical trial data?

Clinical Statistical Programmers often encounter challenges such as handling large and complex datasets, ensuring data integrity, and adhering to strict regulatory guidelines (like CDISC standards). Collaborating closely with biostatisticians, data managers, and clinical teams is essential to resolve data discrepancies and meet tight project deadlines. Staying updated on evolving regulatory requirements and software tools is also vital to maintain high-quality deliverables in a fast-paced environment.

What are the key skills and qualifications needed to thrive as a clinical statistical programmer, and why are they important?

To thrive as a Clinical Statistical Programmer, you need strong proficiency in statistical programming languages (such as SAS or R), a background in statistics or life sciences, and familiarity with clinical trial processes. Experience with CDISC standards (SDTM, ADaM), regulatory submission requirements, and version control systems is highly valued. Attention to detail, problem-solving abilities, and effective communication are crucial soft skills for collaborating with cross-functional teams and ensuring data quality. These skills ensure the accurate analysis and reporting of clinical data, which is critical for regulatory compliance and successful clinical trial outcomes.

What does a clinical statistical programmer do?

A Clinical Statistical Programmer is responsible for managing and analyzing clinical trial data using statistical software, such as SAS or R. Their primary tasks include creating datasets, programming statistical analyses, and generating tables, listings, and figures for clinical study reports. They work closely with biostatisticians and clinical research teams to ensure data accuracy and regulatory compliance. Their work is essential for supporting submissions to regulatory agencies and for making data-driven decisions in clinical research.

What does a clinical statistical programmer do?

The job duties of a clinical statistical programmer involve collecting data, performing statistical analysis, and analyzing data sets according to the needs of their employer or client. Your responsibilities in this career may involve using SAS programming to create and analyze data sets during clinical trials or other clinical experiments and studies. You may also be involved in performing data integration for reports following clinical research or statistical analysis for quality control (QC).

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What are popular job titles related to Clinical Statistical Programmer jobs in AZ? For Clinical Statistical Programmer jobs in AZ, the most frequently searched job titles are:
Clinical Statistical Programmer jobs near you
Infographic showing various Clinical Statistical Programmer job openings in Arizona as of August 2026, with employment types broken down into 2% As Needed, 77% Full Time, 15% Part Time, and 6% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $105,019 per year, or $50.5 per hour.

Scientific Analyst II

University of Arizona

Tucson, AZ

Full-time

Re-posted 22 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

382nd of 617 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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