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Weekend Python Data Analyst Jobs in Tucson, AZ (NOW HIRING)

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer. * Perform statistical analyses including survival analysis, longitudinal ...

Danaher

Digital Analyst Internships

Tucson, AZ · On-site

$91K - $108K/yr

About Digital Analyst Roles at Danaher Are you passionate about data, customer experience, and ... Basic programming or scripting experience in Python, SQL, or JavaScript * Experience with Sitecore ...

Danaher

Digital Analyst Internships

Tucson, AZ

$91K - $108K/yr

About Digital Analyst Roles at Danaher Are you passionate about data, customer experience, and ... Basic programming or scripting experience in Python, SQL, or JavaScript * Experience with Sitecore ...

FocusKPI

Marketing Operations Analyst

Tucson, AZ · On-site

Comfortable with Python. * Experience working within data platforms like Databricks/Snowflake, and analytics modeling platforms such as Tableau * Strong analytical and problem-solving skills with the ...

FocusKPI

Marketing Operations Analyst

Tucson, AZ

Comfortable with Python. * Experience working within data platforms like Databricks/Snowflake, and analytics modeling platforms such as Tableau * Strong analytical and problem-solving skills with the ...

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Weekend Python Data Analyst information

See Tucson, AZ salary details

$32.1K

$78.1K

$128.6K

How much do weekend python data analyst jobs pay per year?

As of Sep 14, 2026, the average yearly pay for weekend python data analyst in Tucson, AZ is $78,134.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,100.00 and $91,700.00 per year, depending on experience, location, and employer.

What is a Weekend Python Data Analyst?

Weekend Python Data Analysts are professionals who work part-time or on weekends to analyze data using Python programming. They typically handle tasks such as cleaning data, performing statistical analyses, creating data visualizations, and generating reports. These analysts often support organizations that require flexible staffing or have projects that need attention outside of regular business hours. Their expertise in Python enables them to efficiently manipulate large datasets and extract actionable insights. This role is ideal for those seeking flexible work arrangements or supplementary income in the data analytics field.

What are the key skills and qualifications needed to thrive as a Weekend Python Data Analyst?

To thrive as a Weekend Python Data Analyst, you need strong analytical skills, proficiency in Python programming, and a background in statistics or data science—often supported by a relevant degree or certification. Familiarity with data visualization tools (like Tableau or Power BI), SQL databases, and Python libraries such as Pandas and NumPy is typically expected. Excellent problem-solving, time management, and communication skills help you interpret data insights and present findings effectively during limited weekend hours. These skills ensure accurate data analysis, actionable recommendations, and efficient collaboration, even within a compressed work timeframe.

What are some common challenges faced by Weekend Python Data Analysts, and how can they be managed?

Weekend Python Data Analysts often face challenges such as limited time to access stakeholders or full datasets, since many team members may not be available outside standard business hours. To manage these challenges, it’s important to communicate needs and data access requirements ahead of time, and to document findings thoroughly for seamless handovers. Being self-sufficient with Python tools and data wrangling is critical, as you may need to troubleshoot issues independently. Proactively setting clear goals for each shift can also help maximize productivity during weekend hours.

What is the difference between Weekend Python Data Analyst vs Weekend Data Scientist?

AspectWeekend Python Data AnalystWeekend Data Scientist
Required SkillsPython, data analysis, visualization, SQLPython, machine learning, statistical modeling, data analysis
CertificationsData analysis certifications, Python certificationsData science certifications, Python certifications
Work EnvironmentPart-time, project-based, remote or on-sitePart-time, project-based, remote or on-site
Industry UsageBusiness analytics, finance, marketingResearch, AI development, advanced analytics

Weekend Python Data Analysts focus on data cleaning, visualization, and basic analysis using Python, suitable for business insights. Weekend Data Scientists handle more complex modeling and machine learning tasks, often requiring advanced statistical skills. Both roles are part-time, flexible, and commonly used across industries, but Data Scientists typically require a deeper technical background.

What cities near Tucson, AZ are hiring for Weekend Python Data Analyst jobs?

Cities near Tucson, AZ with the most Weekend Python Data Analyst job openings:

Weekend Python Data Analyst jobs near you
Infographic showing various Weekend Python Data Analyst job openings in Tucson, AZ as of September 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $78,134 per year, or $37.6 per hour.

Scientific Analyst II

Tucson, AZ • On-site

University of Arizona
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about University of Arizonaexternal

Full-time

Re-posted 26 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

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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