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Entry Level Data Analyst R Programming Jobs in Tennessee

... root cause analysis. Outcome: Master data that enables stable production scheduling and OTIF ... Bachelor's Degree in Supply Chain, Business, Engineering, or related * Job Experience: 2 - 3 years

... data analyst, or another similar role. * You have the ability to clearly communicate with both ... Python and R) * You have experience with several of the following: * Data pipelining and ETL/ELT ...

... root cause analysis. Outcome: Master data that enables stable production scheduling and OTIF ... Bachelor's Degree in Supply Chain, Business, Engineering, or related * Job Experience: 2 - 3 years

INTERN-R&D Lab Tech

Union City, TN · On-site

$15.25 - $20.25/hr

... Record, analyze, and maintain accurate experimental data and lab documentation • Support ... Engineering o Or a related field • Strong interest in polymers, materials science, or ...

INTERN-R&D Lab Tech

Union City, TN · On-site

$15.25 - $20.25/hr

... Record, analyze, and maintain accurate experimental data and lab documentation • Support ... Engineering o Or a related field • Strong interest in polymers, materials science, or ...

$99K - $119K/yr

As a Data Engineer/Analyst, you will work closely with Medisolv clients to extract, transform and load clinical, financial and administrative healthcare data and health plan data to feed our suite of ...

Python, R, SQL, Hive, Spark * Statistical analysis: To understand and work around possible ... Must be proficient in programming either R/Python/Scala. * Should have experience in Financial ...

Showing results 41-60

Entry Level Data Analyst R Programming information

What is an entry level data analyst r programming?

An Entry Level Data Analyst (R Programming) is a professional who uses the R programming language to collect, process, and analyze data to help organizations make informed decisions. They typically work with large datasets, create visualizations, and generate reports under the guidance of more experienced analysts. Entry-level data analysts are often responsible for basic data cleaning, statistical analysis, and supporting team projects while they develop their skills in R and data analysis techniques.

What skills and qualifications are needed to thrive as an entry level data analyst r programming?

To thrive as an Entry Level Data Analyst specializing in R Programming, you need a solid grounding in statistics, data cleaning, and analytical methods, typically supported by a relevant degree such as statistics, mathematics, or computer science. Proficiency in R programming, familiarity with data visualization tools (e.g., ggplot2), and experience with spreadsheet software or SQL are commonly required. Strong attention to detail, problem-solving abilities, and clear communication skills set outstanding candidates apart in this role. These skills are crucial to accurately interpret data, deliver actionable insights, and effectively collaborate with teams to support data-driven decision-making.

What are some typical challenges entry level data analysts face when working with R programming in a team setting?

Entry-level data analysts using R often encounter challenges such as adapting to existing codebases, understanding team-specific data workflows, and ensuring code reproducibility and documentation for collaborative projects. New analysts may also need to quickly learn version control practices (like using Git) and follow standardized procedures for data cleaning and reporting. Regular communication with senior analysts and participation in code reviews are essential to build both technical proficiency and teamwork skills.

What is the difference between Entry Level Data Analyst R Programming vs Data Scientist?

AspectEntry Level Data Analyst R ProgrammingData Scientist
Required SkillsBasic R programming, data cleaning, visualization, ExcelAdvanced R, Python, machine learning, statistical modeling
Work EnvironmentBusiness, finance, marketing teamsResearch, tech, healthcare, diverse industries
CertificationsData analysis, R programming coursesData science, machine learning certifications

Entry Level Data Analyst R Programming roles focus on data cleaning, visualization, and basic analysis using R, often within business environments. Data Scientists require advanced statistical and programming skills, including machine learning, and work on complex predictive models across various industries. While both roles involve data handling, Data Scientists typically have a broader skill set and handle more complex projects.

What are the most commonly searched types of Data Analyst R Programming jobs in Tennessee?

The most popular types of Data Analyst R Programming jobs in Tennessee are:

What are popular job titles related to Entry Level Data Analyst R Programming jobs in Tennessee?

For Entry Level Data Analyst R Programming jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Entry Level Data Analyst R Programming jobs in Tennessee look for?

The top searched job categories for Entry Level Data Analyst R Programming jobs in Tennessee are:

Infographic showing various Entry Level Data Analyst R Programming job openings in Tennessee as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Master Data Specialist

Buckman

Memphis, TN

Full-time

Re-posted 22 days ago


Job description

 

 

Master Data Specialist 

Buckman – Memphis, TN

 

Location: Memphis, TN

Language: English

Travel: up to 5%

Buckman is a privately held, global specialty chemical company with headquarters in Memphis, TN, USA, committed to safeguarding the environment, maintaining safety in the workplace, and promoting sustainable development. Buckman delivers exceptional service and innovative solutions to our customers globally in the pulp and paper, leather, and water treatment sectors to help boost productivity, reduce risk, improve product quality, and provide a measurable return on investment.  

Position Summary 

Own and steward Buckman North America supply chain and manufacturing master data so that planning, scheduling, procurement, manufacturing, quality, and customer fulfillment operate on a single, accurate version of the truth.  

Key Outcomes/Responsibilities

Outcome: Reliable, audit-ready material master supporting planning and execution across 1,876 SKUs. 

Actions:

• Create and maintain material master records (FG, WIP, RM, PFR) with complete MRP, costing, storage, batch/QM, and logistics attributes; enforce naming, classification, and documentation standards.

• Run routine data-quality checks (completeness, duplicates, blocked/inactive, lead-time sanity, UoM consistency); correct defects and prevent recurrence through standards and training.

• Partner with planners/schedulers and plant SMEs to validate key planning parameters (lot size, safety stock, MRP type, procurement type, lead times, rounding values) and align to S&OP policies.

Outcome: Fast, controlled change management for master data with predictable cycle time. 

Actions: 

• Operate a transparent intake/triage workflow for master data requests (create/change/block) with defined SLAs by object type and criticality.

• Perform impact assessment for changes affecting supply, cost, labeling, regulatory/QM, and customer service; coordinate approvals with accountable owners.

• Maintain a change log and version control for critical objects (recipes/BOMs, routings, production versions, QM specs) to enable traceability and post-issue root cause analysis.

Outcome: Master data that enables stable production scheduling and OTIF customer service. 

Actions: 

• Ensure routings, work centers, production versions, and batch sizes reflect plant reality to support realistic promise dates and efficient sequencing.

• Maintain packaging and tolling attributes, alternative supply sources, and PFR relationships so supply planners can execute substitutions and allocations without data rework.

• Monitor execution exceptions tied to master data (MRP messages, ATP failures, batch determination/QM blocks) and eliminate top recurring causes.

Outcome: Improved inventory health and working capital through accurate master data and governance. 

Actions: 

• Maintain accurate shelf-life/expiration, batch management, storage conditions, and disposition rules to minimize write-offs and prevent shipment of nonconforming material.

• Support SLOB and red-tag programs by ensuring correct lifecycle status, obsolescence flags, and substitution rules; coordinate timely blocking/inactivation of obsolete SKUs.

• Validate planning parameters (safety stock, reorder points/ROP, rounding) and lead times using performance data to avoid chronic over/under stocking.

Outcome: High-quality new product and raw material introduction (NPIP / NRIP) and transitions with ‘right-first-time’ data. 

Actions:

• Lead master data readiness for NPIP, NRIP and formula/labeling changes: create end-to-end objects (materials, BOM/recipes, routings, QM inspection plans/specs, packaging, GTIN/UoM) before first production.

• Coordinate cross-functional sign-offs (R&D, Quality, Regulatory, Operations, Planning, Customer Service) for new or changed SKUs.

• Provide cutover plans and data validation checklists for launches, transitions, and phase-outs to prevent order blocks and shipment errors.

Outcome: Standardized data governance, ownership, and controls aligned to supply chain excellence practices. 

Actions:

• Define and maintain master data standards, RACI, and controls (field ownership, required documentation, approval matrix) consistent with APICS-aligned process governance.

• Develop training, job aids, and templates; coach requestors and plant SMEs to improve first-pass quality and reduce rework.

• Support audits and compliance reviews by providing evidence of approvals, change history, and control effectiveness.

Outcome: Actionable visibility of master data performance and continuous improvement. 

Actions: 

• Create and maintain dashboards/scorecards (defect rate, SLA attainment, top defect types, aging requests) using available analytics tools.

• Facilitate routine master data health reviews with Planning, Manufacturing, Quality, and Customer Service; drive corrective actions and systemic fixes.

• Identify automation opportunities (templates, validations, workflows) to reduce manual effort and error rates. 

Basic Qualifications 

  • Education Requirements: Bachelor's Degree in Supply Chain, Business, Engineering, or related
  • Job Experience: 2 - 3 years
  • APICS/ASCM CPIM required or obtained within 12 months
  • ERP / APS systems (SAP required) 

Competencies 

  • Drives Results - Consistently achieving results, even under tough circumstances 
  • Communicates Effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences 
  • Build Networks - Effectively building formal and informal relationship networks inside and outside the organization 
  • Manages Complexity - Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems 
  • Plans and Aligns - Planning and prioritizing work to meet commitments aligned with organizational goals 

We appreciate the interest of recruitment partners, but we are not engaging external agencies for this role.

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