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Data Label Jobs in Tennessee (NOW HIRING)

... and labeling. • Reports to Operations Manager and take daily directions from Technician ... in Data Centers • Shall be able to install ladder racking and seismic bracing both above and ...

Design and implement Microsoft Purview solutions (e.g., sensitivity labeling strategies, advanced DLP policies/integrations, Purview DSPM, data lifecycle/retention controls). Perform threat mapping ...

Develop and maintain data classification and labeling standards. Configure and manage Exact Data Matching (EDM) repositories and detection rules. Implement and optimize endpoint DLP controls across ...

Data Communications Technician No healthcare experience required. Why SWC? SWC combines the ... Route, support, label, and manage low-voltage cabling * Troubleshoot nurse call devices, wiring ...

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Data Label information

What is the difference between Data Label vs Data Annotator?

AspectData LabelData Annotator
Primary RoleAssigns labels to data for machine learning modelsPerforms detailed annotation of data, including labeling and marking specific features
Skills & CertificationsBasic understanding of data types, labeling toolsMore detailed annotation skills, familiarity with annotation tools
Work EnvironmentData labeling platforms, remote or on-siteAnnotation tools, often similar to labeling platforms
Industry UsageUsed across AI, machine learning, and data science projectsUsed in similar fields, often with more complex annotation tasks

Data Label and Data Annotator roles are closely related, with Data Labeling focusing on assigning simple labels to data, while Data Annotators perform more detailed and complex annotations. Both roles are essential in preparing data for AI and machine learning, often using similar tools and working within the same industry environments.

What are some common challenges faced by Data Labelers, and how can they be addressed?

Data Labelers often face challenges such as handling large volumes of repetitive data, maintaining high accuracy under tight deadlines, and quickly adapting to changing labeling guidelines. To address these challenges, it's important to develop strong attention to detail, use quality control processes like regular peer reviews, and communicate proactively with team leads if guidelines are unclear. Additionally, many teams use specialized annotation tools to streamline workflows and minimize errors, making it helpful to become familiar with these platforms.

What are the key skills and qualifications needed to thrive as a Data Labeler, and why are they important?

To thrive as a Data Labeler, you need strong attention to detail, basic computer literacy, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Experience with labeling platforms, annotation tools, and sometimes knowledge of data management systems is typically required. Reliability, consistency, and the ability to follow precise instructions are the soft skills that set top performers apart. These skills ensure accurate and high-quality data labeling, which is critical for training effective machine learning models.

What are data labelers?

Data labelers are professionals who annotate or tag data—such as images, text, or audio—to provide context and structure for use in machine learning and artificial intelligence projects. Their work involves identifying and labeling key features in raw data so that algorithms can learn to recognize patterns and make predictions. Data labeling is a crucial step in training supervised learning models, ensuring the accuracy and effectiveness of AI systems.
What are popular job titles related to Data Label jobs in Tennessee? For Data Label jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Data Label jobs in Tennessee look for? The top searched job categories for Data Label jobs in Tennessee are:

Job description

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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