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Afternoon Data Analyst R Programming Jobs in Tucson, AZ

AirLife

Data Analyst - Commercial

Tucson, AZ · On-site

Experience with SQL, Python, R or other programming tools. * Experience with project management ... Project manage key data analytics initiatives by leading requirements gathering, coordinating ...

Varsity Tutors

Data Science Tutor

Tucson, AZ · Remote

$40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

The position requires advanced expertise in data science, artificial intelligence, and machine ... Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn ...

Succeed ABA Arizona

RBT AFTERNOON HOURS

Tucson, AZ

$18 - $28/hr

Registered Behavior Technician (RBT) AFTERNOON HOURS Pay: $18-$28 per hour Job Type: Part and ... Data collection, analysis, and preparation for presentation * Coordinate with other program staff ...

Succeed ABA Arizona

BT AFTERNOON HOURS

Tucson, AZ

$19 - $26/hr

BT AFTERNOON HOURS About Succeed ABA: Succeed ABA is an ABA Therapy provider for children with ... Data collection, analysis and preparation for presentation * Coordinate with other program staff to ...

DataAnnotation

Software Engineer Analyst

Tucson, AZ · Remote

$40 - $75/hr

... take on programming tasks that include creating and solving challenging coding problems, building beautiful apps with rich functionality, and synthesizing insights through data analysis and ...

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 ...

FocusKPI

Marketing Operations Analyst

Tucson, AZ · On-site

Data Integration: Collaborate with cross-functional teams such as IT and engineering to integrate data from various sources into the customer data platform (CDP) for comprehensive analysis.

Stantec

Senior Data Architect

Tucson, AZ · On-site

$62.50 - $83.75/hr

... developers, Business Analysts, and Project Management - to plan and execute PM2PA data-related ... projects. Act as the bridge between technical teams and business stakeholders for all matters ...

Rain Bird Corporation

Sr. Quality Engineer

Tucson, AZ

$83K - $113K/yr

Leads activities to analyze quality assurance data and initiates root cause and corrective action ... Knowledge of Pareto analysis techniques, Gage R&R, FAI, PPAP, Control Plans and FMEA. * Skilled in ...

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All JobsAfternoon Data Analyst R Programming JobsAfternoon Data Analyst R Programming Jobs in Tucson, AZ

Afternoon Data Analyst R Programming information

See Tucson, AZ salary details

$32.1K

$78.1K

$128.6K

How much do afternoon data analyst r programming jobs pay per year?

As of Jun 15, 2026, the average yearly pay for afternoon data analyst r programming 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 an Afternoon Data Analyst R Programming?

An Afternoon Data Analyst specializing in R Programming is a data professional who primarily works afternoon shifts and uses the R programming language to analyze, interpret, and visualize data. Their responsibilities typically include cleaning data, performing statistical analyses, and generating reports to support business decisions. They may work across various industries, collaborating with teams to provide insights and automate data processes using R. Afternoon shifts can be ideal for organizations that operate globally or require data support outside standard business hours. Proficiency in R, statistical techniques, and data visualization tools are essential skills for this role.

What are some common challenges faced by Afternoon Data Analysts working with R Programming, and how can they be addressed?

Afternoon Data Analysts using R Programming often encounter challenges such as handling large datasets efficiently, ensuring code reproducibility, and collaborating with team members across different shifts. To address these, it's helpful to utilize R packages designed for big data (like data.table or dplyr), maintain clear and well-documented scripts, and use version control systems like Git for seamless collaboration. Regular communication with team members during shift handovers and leveraging collaborative tools can also enhance workflow and reduce misunderstandings.

What is the difference between Afternoon Data Analyst R Programming vs Morning Data Analyst R Programming?

AspectAfternoon Data Analyst R ProgrammingMorning Data Analyst R Programming
Required CredentialsBachelor's in Data Science, Statistics, or related field; R programming skillsBachelor's in Data Science, Statistics, or related field; R programming skills
Work EnvironmentTypically in office settings, working during afternoon hoursOffice environment, working during morning hours
Employer & Industry UsageUsed in industries with shift-based operations like finance, healthcareCommon in similar industries, often with flexible scheduling
Search & Comparison IntentPeople comparing different shift roles or schedules in data analysisSimilar search intent focusing on shift timing differences

The main difference between Afternoon Data Analyst R Programming and Morning Data Analyst R Programming lies in their work hours. Both roles require similar skills, credentials, and are used in comparable industries. The choice depends on personal schedule preferences and employer shift structures.

What are the key skills and qualifications needed to thrive as an Afternoon Data Analyst specializing in R Programming, and why are they important?

To thrive as an Afternoon Data Analyst specializing in R Programming, you need a strong background in statistics, data analysis, and proficiency with R, often supported by a degree in a quantitative field. Experience with data visualization tools, R packages (like tidyverse), and familiarity with databases or version control systems (such as Git) is typically required. Critical thinking, attention to detail, and effective communication are essential soft skills for interpreting results and presenting insights to stakeholders. These skills ensure accurate data-driven decisions, efficient workflow, and the ability to translate complex data into actionable business strategies.
What are the most commonly searched types of Data Analyst R Programming jobs in Tucson, AZ? The most popular types of Data Analyst R Programming jobs in Tucson, AZ are:
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What job categories do people searching Afternoon Data Analyst R Programming jobs in Tucson, AZ look for? The top searched job categories for Afternoon Data Analyst R Programming jobs in Tucson, AZ are:
What cities near Tucson, AZ are hiring for Afternoon Data Analyst R Programming jobs? Cities near Tucson, AZ with the most Afternoon Data Analyst R Programming job openings:
Afternoon Data Analyst R Programming jobs near you
Senior Biorepository Data Analyst

Senior Biorepository Data Analyst

University of Arizona

Tucson, AZ • On-site

$81K - $102K/yr

Other

Posted 16 days ago

University Of Arizona rating

7.1

Company rating: 7.1 out of 10

Based on 66 frontline employees who took The Breakroom Quiz

351st of 537 rated colleges and universities

Job description

Data Analytics

  • Gathers, structures and analyzes data from various databases and sources, performs statistical and/or mathematical programming, develops reports and visual representations.
  • Collaborates with interdisciplinary teams to design and implement research projects, with emphasis on phenotyping and clinical data analytics.
  • Extracts, cleans, and organizes biomedical data from multiple sources, including biospecimen, genomic data, imaging data, research study data, and clinical data.
  • Queries clinical, biospecimen, genomic and other biomedical database to support research questions and operational reporting needs.
  • Utilizes software (e.g., R, Python, SAS, etc.) to conduct advanced data analysis and support phenotypic data insights.

Data Management

  • Creates and maintains databases and tools to store and manage data that meet research needs as well as regulatory and organizational standards.
  • Develops and maintains data interfaces and APIs to support optimized data workflows.
  • Develops and updates documentation, standard operating procedures, and guidelines for systems and databases.

Data Quality and Regulatory Compliance

  • Participates in data quality audits and implements improvements in collaboration with the Data Quality, and U of A regulatory teams.
  • Ensures data integrity, security, and confidentiality following HIPAA regulations and organizational policies.
  • Demonstrates an understanding of and proficiency with the application of all compliance and reporting requirements respective HIPAA and OHRP.
  • Advises and guides PIs and other investigators on data regulatory issues.
  • Complies with all data and operational standards of the IT, BI and Informatics Departments.

Researcher Support

  • Provides training for staff involved in data management and research analytics, supporting capacity-building efforts.
  • Provides expert level consultation services to researchers.

Other

  • May provide mentoring or supervision to students, or junior research staff.
  • Make recommendations for improvement to management.

Knowledge, Skills, and Abilities:

  • Knowledge of HIPAA and human subjects research data regulatory requirements.
  • Ability to gather, listen and respond to feedback to ensure the data system meets stakeholder needs.
  • Strong organizational skills, ability to work independently and meet project deadlines.
  • Strong data query analysis skills including SQL..
  • Knowledge of healthcare coding systems (e.g., ICD, LOINC, RxNorm, CPTetc.).
  • Knowledge of Epic data structures and query tools (e.g. clarity, caboodle, cogito).
  • Knowledge of biobanking software and workflows.

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