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Hourly Remote Data Labeling Jobs in Dallas, TX (NOW HIRING)

Jobsultant Solutions

Remote Data Analyst

Dallas, TX · Remote

Work Environment: * 100% remote - must work in CST * 8am-5pm (9 hour day with one hour lunch break ... Own Brand (private label) reporting, recapping, tracking, and analysis, including ad-hoc analysis ...

HumanSignal

High Volume (TOFU) Recruiter

Dallas, TX · On-site +1

$55K - $100K/yr

... standard for data labeling and evaluation, used by over 1 million practitioners worldwide. We ... San Francisco, CA preferred; open to other remote options About the Role HumanSignal Services runs ...

HumanSignal

Delivery Lead

Dallas, TX · Remote

$110K - $140K/yr

... and remote workforce marketplaces can't. We own projects end-to-end, from scoping and protocol ... Our work spans RLHF, evals, red-teaming, and custom multimodal data creation, all powered by Label ...

Lowe's

National Account Manager

Dallas, TX · Remote

$83K - $143K/yr

... data and strong understanding of financials, profitability, and cash flow. About Lowe's Lowe's ... Lowe's hourly remote associates cannot reside in Alaska, California or Hawaii. Lowe's salaried ...

1 point system

Cyber Data Security

Plano, TX · Remote

Requirement - Cyber Data Security Location- 100% Remote Contract W2 Description Summary We are ... Provide support to Collaboration team to place data classification labels on data. * Provide ...

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Hourly Remote Data Labeling information

What are the key skills and qualifications needed to thrive as an hourly remote data labeler?

To thrive as an Hourly Remote Data Labeler, you need strong attention to detail, basic computer literacy, and the ability to follow specific guidelines, often with a high school diploma or equivalent. Familiarity with data annotation tools and platforms such as Labelbox, Prodigy, or internal company systems is typically required. Reliability, time management, and effective written communication are crucial soft skills for meeting deadlines and maintaining quality in a remote setting. These skills and qualities are important to ensure accurate, consistent data labeling that directly impacts the performance of AI and machine learning models.

What is the difference between Hourly Remote Data Labeling vs Data Annotation Specialist?

AspectHourly Remote Data LabelingData Annotation Specialist
CredentialsBasic computer skills, attention to detailSimilar credentials, often with some industry-specific knowledge
Work EnvironmentRemote, flexible hoursRemote, often project-based or ongoing
Industry UsageCommon in AI/ML developmentUsed across tech, healthcare, automotive sectors
Search IntentLooking for remote data labeling jobsSearching for data annotation roles

Both roles involve labeling or annotating data for machine learning models, often remotely. The main difference lies in terminology and specific industry usage, but they share similar credentials and work environments.

What is hourly remote data labeling?

Hourly remote data labeling is a job where individuals work from home to tag, categorize, or annotate data (such as images, videos, text, or audio) for machine learning and artificial intelligence projects. Workers are typically paid by the hour and use online platforms to complete labeling tasks assigned by companies or research organizations. This work is crucial because AI models need large volumes of accurately labeled data to learn and function properly. The job usually requires attention to detail and may involve following specific guidelines to ensure data quality.

What are some common challenges faced by hourly remote data labelers, and how can they be managed?

Hourly remote data labeling professionals often encounter challenges such as maintaining consistent accuracy, managing repetitive tasks, and staying self-motivated while working independently. To manage these challenges, it's important to set up a dedicated workspace, take regular breaks to reduce fatigue, and follow established labeling guidelines closely. Frequent communication with team leads and participating in quality feedback sessions can also help ensure your work meets project standards and fosters professional growth.

What are popular job titles related to Hourly Remote Data Labeling jobs in Dallas, TX?

For Hourly Remote Data Labeling jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Hourly Remote Data Labeling jobs in Dallas, TX look for?

The top searched job categories for Hourly Remote Data Labeling jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Hourly Remote Data Labeling jobs?

Cities near Dallas, TX with the most Hourly Remote Data Labeling job openings:

Hourly Remote Data Labeling jobs near you
Infographic showing various Hourly Remote Data Labeling job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Remote Data Analyst

Jobsultant Solutions

Dallas, TX • Remote

Contractor

Re-posted 2 days ago

Job description

The Nations 2nd largest Technical Staffing and Services Firm, has an opening for a Remote Analyst at a global leader in retail pharmacy for a 6-month contract with the opportunity for extension or conversion.

Summary:

The Walgreens Merchandise Planning Analytics team manages reporting, analysis, and tool building for the Walgreens Merchandising team. They support a team of 100+ merchants and 40+ planners to optimize performance of the Walgreens Front End (non-Pharmacy) businesses.

Work Environment:

  • 100% remote - must work in CST
  • 8am-5pm (9 hour day with one hour lunch break 12pm-1pm).
    • The hours can be flexible per above but minimally need 30 hours a week in order to fulfill the business needs

Responsibilities:

  • Own Brand (private label) reporting, recapping, tracking, and analysis, including ad-hoc analysis based on business needs
  • Item level historical analysis and recapping
  • Item level future looking forecast
  • Scope will be thousands of Own Brand items (at item level)
  • System/ tool used is Excel
  • Project will involve complex excel formulas and ability to synthesize large data sets into meaningful executive recaps

Top Must-Have Skills:

  • Bachelors Degree and at least 3-5 year of experience in one or more of the following: analytics, data science, finance, financial analysis, assortment planning, retail, procurement/purchasing, merchandising
  • Minimum 3 years experience in data analytics or other related field with strong understanding of retail merchandising basics (i.e. category management) and merchandising organization structures and processes
  • Outstanding analytical and problem-solving skills partnered with storytelling finesse
  • Ability to conduct complex quantitative analysis and build analytical models using Microsoft Excel
  • Ability to communicate effectively and succinctly both verbally and in writing via Microsoft PowerPoint
  • Ability to manage and prioritize multiple assignments and meet tight deadlines
  • Ability to develop effective working relationships with Merchandising, Planning, Finance and other stakeholders
  • Ability to work independently and adapt to changing work priorities
  • Retail math background; skills such as item level forecasting based on demand patterns or sell through/ sales/ profit goals, knowledge of sales by type and AUR impacts, all inventory and sales metrics
  • Intermediate/Advanced level skill in Microsoft Excel (for example: using SUM/SUMIFS, Subtotals, VLOOKUP, HLOOKUP, Macros, Pivot tables) - CRITICAL SKILL NEEDED FOR ROLE, most work will be done in excel
  • Intermediate level skill in Microsoft Word and PowerPoint

Top Nice-to-Have Skills:

  • MBA preferred; Science, Technology, Engineering, Mathematics and/or other quantitative analytical degree a plus
  • Data science experience
  • Retail experience
  • SQL Coding, Microsoft Access, Macro building

Education / Experience Requirements:

  • 3-5 year experience in Analytics or Data Science, Merchandising background

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