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Scientific Manager Jobs in Arizona (NOW HIRING)

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

Epidemiological and Clinical Data Management and Harmonization: * Access, curate, harmonize, and ... Scientific Communication, Dissemination, and Collaboration: * Compare and contribute to peer ...

NEOGOV

ERP Role and Workflow Analyst I/II

Mesa, AZ · On-site

Required : • Graduation from an accredited college or university with a Bachelor's Degree in Computer Science, Management Information Systems, Business Administration, Accounting, or closely ...

Pwc

Payer Healthcare Data Scientist, Manager

Phoenix, AZ · On-site

$99K - $232K/yr

Industry/Sector Health Services Specialism Operations Management Level Manager & Summary At PwC ... The Opportunity As part of the Operations Consulting team, you will apply advanced data science and ...

Deloitte

Senior AI Engineer - SFL Scientific

Tempe, AZ · On-site

$100K - $137K/yr

SFL Scientific, a Deloitte Business, is looking to add a Senior AI Engineer to their vibrant ... Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment * Strong ...

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Showing results 21-40

All JobsScientific JobsScientific Manager JobsScientific Manager Jobs in Arizona

Scientific Manager information

See Arizona salary details

$27.7K

$89K

$145.1K

How much do scientific manager jobs pay per year?

As of Sep 2, 2026, the average yearly pay for scientific manager in Arizona is $89,023.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,563.00 and $111,650.00 per year, depending on experience, location, and employer.

What is a scientific manager?

Scientific Managers are professionals who oversee scientific research projects, teams, or departments within organizations such as universities, research institutes, or private companies. They coordinate research activities, manage budgets and resources, ensure compliance with regulations, and often serve as a bridge between scientists and administrative staff. Scientific Managers may also help set research priorities, facilitate communication among stakeholders, and support the professional development of research staff. Their role is essential in ensuring that scientific projects are completed efficiently, on time, and within budget.

What are some common challenges scientific managers face when leading multidisciplinary research teams?

Scientific Managers often navigate the complexities of coordinating team members from diverse scientific backgrounds, each with their own methodologies and communication styles. Ensuring clear project goals, facilitating effective collaboration, and managing timelines can be challenging, especially in fast-paced research environments. Additionally, balancing administrative duties with scientific oversight requires strong organizational and leadership skills. Successful Scientific Managers foster a culture of open communication and continuous learning to overcome these challenges.

What are the key skills and qualifications needed to thrive as a scientific manager, and why are they important?

To thrive as a Scientific Manager, you need a strong background in scientific research, project management experience, and typically an advanced degree (such as a PhD or MSc) in a relevant field. Familiarity with laboratory information management systems (LIMS), data analysis software, and compliance with regulatory standards is crucial. Leadership, strategic thinking, and excellent communication skills distinguish top performers in this role. These skills and qualifications are vital to effectively oversee research teams, ensure project success, and bridge the gap between science and organizational objectives.

What is the difference between Scientific Manager vs Research Scientist?

AspectScientific ManagerResearch Scientist
Required CredentialsAdvanced degrees (Master's or PhD), management trainingTypically PhD or Master's in a scientific field
Work EnvironmentOversees projects, manages teams, strategic planningConducts experiments, data analysis, publishes findings
Employer & Industry UsageResearch institutions, biotech, pharma companiesUniversities, research labs, industry R&D
Common Search & ComparisonOften compared for leadership roles in scienceCompared for hands-on research roles

The main difference is that Scientific Managers focus on overseeing research projects and managing teams, while Research Scientists are primarily involved in conducting experiments and generating scientific data. Both roles require advanced degrees, but Scientific Managers also need leadership and management skills to coordinate research efforts effectively.

What are the most commonly searched types of Scientific jobs in Arizona?

The most popular types of Scientific jobs in Arizona are:

What are popular job titles related to Scientific Manager jobs in Arizona?

For Scientific Manager jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Scientific Manager jobs in Arizona look for?

The top searched job categories for Scientific Manager jobs in Arizona are:

What cities in Arizona are hiring for Scientific Manager jobs?

Cities in Arizona with the most Scientific Manager job openings:

Scientific Manager jobs near you
Infographic showing various Scientific Manager job openings in Arizona as of August 2026, with employment types broken down into 87% Full Time, 10% Part Time, 2% Temporary, and 1% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $89,023 per year, or $42.8 per hour.

Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Full-time

Re-posted 14 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 628 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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