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Afternoon Data Analyst R Programming Jobs in Columbus, OH

Pwc

Data Strategy-Manager

Columbus, OH

$99K - $232K/yr

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All JobsAfternoon Data Analyst R Programming JobsAfternoon Data Analyst R Programming Jobs in Columbus, OH

Afternoon Data Analyst R Programming information

See Columbus, OH salary details

$32.8K

$79.8K

$131.4K

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

As of Jul 15, 2026, the average yearly pay for afternoon data analyst r programming in Columbus, OH is $79,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,400.00 and $93,700.00 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 popular job titles related to Afternoon Data Analyst R Programming jobs in Columbus, OH? For Afternoon Data Analyst R Programming jobs in Columbus, OH, the most frequently searched job titles are:
What job categories do people searching Afternoon Data Analyst R Programming jobs in Columbus, OH look for? The top searched job categories for Afternoon Data Analyst R Programming jobs in Columbus, OH are:
What cities near Columbus, OH are hiring for Afternoon Data Analyst R Programming jobs? Cities near Columbus, OH with the most Afternoon Data Analyst R Programming job openings:
Afternoon Data Analyst R Programming jobs near you
Physical Security Systems Audit & Data Analyst

Physical Security Systems Audit & Data Analyst

Blue Star Partners LLC

Columbus, OH • On-site

$35 - $55/hr

Contractor

Re-posted 25 days ago

Job description

Job Title: Physical Security Systems Audit & Data Analyst
Location: Columbus, OH (Hybrid: 3 times a week Tuesday - Thursday)
Duration: 1-Year Contract-to-Hire
Rate: $35 – $55/hr (W2)
Work Authorization: Must be authorized to work in the United States now and in the future without the need for employment-based visa sponsorship. Sponsorship is not available for this position.

Position Overview

We are seeking a Physical Security Systems Audit & Data Analyst to ensure the accuracy, integrity, and ongoing quality of physical security systems and related data across the enterprise. This role is responsible for auditing physical security platforms, validating data consistency, identifying discrepancies, and supporting long-term system reliability.

This position is ideal for someone with a strong audit, assurance, or data validation background who is highly detail-oriented and enjoys identifying anomalies, reconciling data, and improving system quality. The role is not focused on regulatory compliance, but rather on ensuring physical security systems are configured correctly, data is accurate, and records are clean and defensible.

The analyst will partner closely with Physical Security Engineering, Cybersecurity, IT, Facilities, and vendors to support a mature and well-governed physical security environment.

Technologies Utilized
  • SiteOwl
  • CCure
  • Avigilon
  • SureView
  • Microsoft Excel
  • Microsoft Office Suite (Word, Outlook, PowerPoint)
Required Qualifications
  • 4+ years of experience in audit, assurance, data validation, risk analysis, or control testing
  • Strong analytical skills with experience reviewing large and complex datasets
  • Demonstrated ability to identify inconsistencies, anomalies, and data quality issues
  • Exceptional attention to detail and accuracy
  • Experience working in structured, process-driven environments
  • Ability to manage multiple review efforts simultaneously without sacrificing quality
  • Strong documentation skills with the ability to clearly record findings, recommendations, and outcomes
  • Advanced proficiency with Microsoft Excel and strong working knowledge of Word, Outlook, and PowerPoint
  • Strong problem-solving and troubleshooting skills
  • Clear and effective written and verbal communication skills
Preferred Qualifications
  • Background in corporate tax audit, internal audit, financial audit, or assurance functions
  • Experience performing reconciliations between systems, applications, or databases
  • Familiarity with physical security systems, access control concepts, or cybersecurity fundamentals
  • Experience working with technologies such as SiteOwl, CCure, Avigilon, or SureView
  • Exposure to IT systems, identity/access management, or operational monitoring environments
Key ResponsibilitiesSystem Audits & Data Validation
  • Perform detailed audits of physical security systems including access control, video management, and alarm platforms
  • Validate data accuracy and completeness across multiple physical security tools and systems of record
  • Reconcile users, permissions, devices, alarms, and configuration data between upstream and downstream systems
  • Identify discrepancies, misconfigurations, orphaned records, outdated entries, and systemic data issues
Data Integrity & Governance
  • Ensure physical security systems remain clean, consistent, and trustworthy through recurring review and validation
  • Verify that system configurations and data align with established internal standards and approved designs
  • Track and document findings, corrections, and validation outcomes to support long-term system reliability
Issue Analysis & Remediation Support
  • Analyze root causes of data inconsistencies and recurring errors
  • Work with Physical Security Engineering, Cybersecurity, IT, Facilities, and vendors to resolve identified issues
  • Confirm corrective actions are accurately implemented and sustained over time
Process & Quality Improvement
  • Identify patterns, trends, and systemic weaknesses in system data or configurations
  • Support improvements to processes that reduce manual error, improve repeatability, and enhance auditability
  • Help develop structured review routines, validation checklists, and standard operating procedures
Reporting & Communication
  • Prepare clear, concise summaries of findings, discrepancies, trends, and corrective actions for internal teams and leadership
  • Translate technical system information into understandable observations and recommendations
  • Communicate issues in a factual, evidence-based manner with clarity and precision
Learning & Technical Development
  • Develop a working knowledge of physical security technologies, system architecture, and data flows through training and hands-on experience
  • Learn how physical security tools integrate with cybersecurity platforms, identity systems, and operational monitoring processes
Key Attributes for Success
  • Meticulous, methodical, and highly detail-oriented
  • Comfortable questioning data, validating assumptions, and investigating inconsistencies
  • Strong sense of ownership for data quality and system accuracy
  • Organized, disciplined, and process-driven
  • Able to work independently while collaborating effectively with technical and business teams
  • Strong curiosity and willingness to learn new technologies and systems
Success in This Role
  • Physical security systems and records remain accurate, clean, and reliable
  • Data discrepancies and configuration issues are identified and resolved proactively
  • Internal teams have greater confidence in the integrity of system data and reporting
  • Processes become more repeatable, auditable, and less prone to error