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Afternoon Data Analyst R Programming Jobs in Green Bay, WI

Summary As a Data Engineer on the Data Analytics Data Platform (DADP) team, you analyze, develop, and deliver business intelligence solutions primarily using Google Cloud Platform (GCP) data platform ...

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... multiple programming languages, frameworks, and technologies, specifically Python, SQL, R, and ... Advanced analytical skills with an emphasis on attention to detail and being able to look at a ...

Data Engineer II

Green Bay, WI · On-site

$111K - $133K/yr

... analytical and operational use cases • Optimize data storage and retrieval mechanisms for ... engineering, information systems, or related field or equivalent experience; master's degree ...

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Afternoon Data Analyst R Programming information

See Green Bay, WI salary details

$33.1K

$80.4K

$132.3K

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

As of Jul 17, 2026, the average yearly pay for afternoon data analyst r programming in Green Bay, WI is $80,381.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,800.00 and $94,300.00 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 Green Bay, WI? For Afternoon Data Analyst R Programming jobs in Green Bay, WI, the most frequently searched job titles are:
What job categories do people searching Afternoon Data Analyst R Programming jobs in Green Bay, WI look for? The top searched job categories for Afternoon Data Analyst R Programming jobs in Green Bay, WI are:
What cities near Green Bay, WI are hiring for Afternoon Data Analyst R Programming jobs? Cities near Green Bay, WI with the most Afternoon Data Analyst R Programming job openings:
Senior Operational Excellence Data Analyst

Senior Operational Excellence Data Analyst

Schreiber Foods

Green Bay, WI • On-site

$83K - $105K/yr

Full-time

Re-posted 24 days ago


Schreiber Foods rating

8.2

Company rating: 8.2 out of 10

Based on 75 frontline employees who took The Breakroom Quiz

62nd of 397 rated food and drinks producers


Job description

Job Summary:
Schreiber Foods is seeking a Senior Operational Excellence Data Analyst who will be responsible for achieving operational and functional targets through independent ownership of moderately complex projects. This role involves collaborating with various teams to enhance operational performance and implementing improvements to processes and systems.
Responsibilities:
• Develop and Sustain SFI Data & Operational Excellence Culture Support, coach, and reinforce data-driven decision making and Operational Excellence principles with partners and Team Members.
• Operational Data Enablement & Business Partnership Serve as the bridge between Operations and Schreiber’s technical data teams to translate complex data into actionable insights.
• Partner closely with operations, manufacturing engineering, process engineering, sourcing, packaging engineering, and FP&A to enable plant teams to identify and act on opportunities related to quality, delivery, capacity, and cost.
• Production Data Needs & Use-Case Definition Understand, define, and prioritize the data needs of production teams to support effective business decisions.
• Apply structured improvement methodologies (e.g., Lean Six Sigma) to support cost, quality, and operational performance initiatives through disciplined data use.
• Data Structure Development, Collection, and Preparation Develop, standardize, and maintain data structures and data pipelines that enable reliable data collection from multiple sources.
• Ensure data accuracy, completeness, and consistency to support trusted analytics and KPI reporting.
• Data Exploration, Analysis, and Model Development Apply analytical and statistical methods to explore data, identify trends, and uncover root causes.
• Where appropriate, develop and maintain models to forecast performance and support proactive, data-based decision making.
• Data Visualization, KPI Standards, and Reporting Establish standards and develop clear, intuitive data visualizations using tools such as Power BI.
• Translate insights into visual formats that are easily understood and actionable, including leadership dashboards and plant-floor visualizations (e.g., HMI screens), to drive alignment and execution.
• Training, Enablement, and Capability Building Provide targeted training, standards, and enablement that build Operations’ capability to independently access, analyze, and visualize data.
• Empower partners to adopt best practices in data analytics and visualization to sustain results beyond direct support.
• Cross-Functional Collaboration & Continuous Improvement Leadership Actively collaborate across functions to promote best practices in data access, analytics, and KPI visualization.
• Champion continuous improvement through effective use of data, analytics, and insight-driven problem resolution.
• May be required to perform other job-related tasks.
Qualifications:
Required:
• Bachelor’s degree in Engineering, Data Analytics, Operations or related technical field.
• 7+ years experience in manufacturing/operations, engineering, technical or related area in building and interpreting operational metrics, dashboards, and analysis.
• Proficiency in Excel, SQL, Power BI, and/or Tableau
• Knowledge/proficiency of statistical programming languages like Phyton or commitment to develop proficiency within 12-24 months is expected.
• Data collection, cleaning and governance practices
• Lean and Six Sigma disciplines where applicable
• Problem-solving skills and ability to translate data into action.
• Ownership, self-started mindset; servant mindset
• Desire to grow and take on new challenges and opportunities.
• Travel varies depending on manufacturing needs. Ability to travel 20% - 40% as required.
• Valid driver's license, auto insurance (at least state minimum- more might be required), acceptable driving record per Schreiber Foods discretion, and vehicle that will ensure applicant can meet the travel necessities of the position are required.
• Authorization to work in the country in which the role is based.
Company:
Schreiber provides dairy favorites to people around the globe. Founded in 1945, the company is headquartered in Green Bay, USA, with a team of 10001+ employees. The company is currently Late Stage.

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