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Remote Data Labeling Analyst Jobs in Texas (NOW HIRING)

VetsEZ

Data Engineer (Remote Opportunity)

Austin, TX · On-site +1

$113K - $136K/yr

We are currently looking for a Data Engineer for a 100% remote position on a large federal ... Develop and support data analytics and reporting solutions using Power BI. * Ensure data quality ...

Orionyx Enginnering

HR Data Analyst

El Paso, TX · Remote

$60.50 - $65.50/hr

This is a remote role strictly for candidates within the United States. We are looking for an experienced HR Data Analyst to join ORIONYX ENGINEERING LTD . In this role, you will be responsible for ...

Launch Potato

Lead Data Analyst, Marketing

Austin, TX · Remote

$120K - $150K/yr

We run profitable and bootstrapped, with a South Florida HQ and a remote-first team across 18 ... analyses, and connect data work to decisions that actually moved the business. YOUR ROLE Launch ...

VytlOne

Sr. Epic Data Acquisition Analyst

Plano, TX · Remote

$82K - $103K/yr

Position Location This is a remote-based position within the Continental US. Who We Are VytlOne is ... Coordinate with VytlOne data platform, analytics, and 340B business teams, and with external ...

CSAA

Job Title Regulatory Reporting Analyst- Remote Requisition Number R7892 Regulatory Reporting ... Analyze regulatory data against statutory financial statements and prior regulatory filings to ...

New

HumanSignal

High Volume (TOFU) Recruiter

Austin, 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 ...

PTP

Customer Experience Analyst - REMOTE

Austin, TX · On-site +1

About Us: * Customer Experience Analyst - REMOTE PTP is a fast-growing system integrator that ... Analysis and Data Optimization: Analyze large datasets and conversational call flows to uncover ...

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

Austin, 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 ...

Resource Innovations

Energy Engineer

Dallas, TX · On-site +1

Responsibilities may include remote data analysis, desktop engineering review, savings calculations, measure validation, economic analysis, incentive review, field investigation, and documentation of ...

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 ...

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All JobsData Labeling Analyst JobsRemote Data Labeling Analyst JobsRemote Data Labeling Analyst Jobs in Texas

Remote Data Labeling Analyst information

What are the key skills and qualifications needed to thrive as a remote data labeling analyst?

To thrive as a Remote Data Labeling Analyst, you need strong attention to detail, analytical thinking, and basic data management skills, typically supported by a high school diploma or higher. Familiarity with annotation tools, data labeling platforms, and sometimes basic programming or spreadsheet software is required. Strong communication, time management, and the ability to work independently are crucial soft skills for excelling remotely. These abilities ensure high-quality, accurate data labeling that directly impacts the effectiveness of AI and machine learning systems.

What are some common challenges faced by remote data labeling analysts, and how can they be addressed?

Remote Data Labeling Analysts often encounter challenges such as maintaining focus during repetitive tasks, managing time effectively across multiple projects, and ensuring high accuracy in labeling complex data sets. To address these challenges, it is helpful to follow structured workflows, take regular breaks to reduce fatigue, and leverage collaboration tools to communicate with team members for clarification or feedback. Staying updated with labeling guidelines and participating in regular training sessions can also help improve both productivity and quality of work.

What does a remote data labeling analyst do?

A Remote Data Labeling Analyst is responsible for reviewing, tagging, and annotating data—such as images, videos, text, or audio—to help train machine learning models. Working remotely, they use specialized software to classify or categorize this data according to specific guidelines. Their work is crucial for improving the accuracy and performance of artificial intelligence systems, as well-labeled data enables the AI to learn and make better predictions. This role typically requires attention to detail, consistency, and the ability to follow complex instructions.

What is the difference between Remote Data Labeling Analyst vs Remote Data Annotator?

AspectRemote Data Labeling AnalystRemote Data Annotator
CredentialsBasic data labeling skills, familiarity with annotation toolsSimilar credentials, often entry-level
Work EnvironmentRemote, often part of a data teamRemote, typically individual tasks
Industry UsageUsed across AI, machine learning, and data science companiesCommon in AI training data preparation
Job FocusLabeling and categorizing data for machine learningAnnotating data with labels or tags

The Remote Data Labeling Analyst and Remote Data Annotator roles are similar, both involving data labeling tasks in a remote setting. The Analyst may have additional responsibilities like quality checks or data management, but both positions require similar skills and are used widely in AI and machine learning industries.

What are the most commonly searched types of Data Labeling Analyst jobs in Texas? The most popular types of Data Labeling Analyst jobs in Texas are:
What are popular job titles related to Remote Data Labeling Analyst jobs in Texas? For Remote Data Labeling Analyst jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Remote Data Labeling Analyst jobs in Texas look for? The top searched job categories for Remote Data Labeling Analyst jobs in Texas are:
What cities in Texas are hiring for Remote Data Labeling Analyst jobs? Cities in Texas with the most Remote Data Labeling Analyst job openings:
Remote Data Labeling Analyst jobs near you
Infographic showing various Remote Data Labeling Analyst job openings in Texas as of July 2026, with employment types broken down into 1% Internship, 86% Full Time, 7% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Re-posted 6 days ago

Job description

NAVA Software solutions is looking for a Data Scientist
Details:
Data Scientist
Location: Houston TX - Remote is ok
Duration: 12 months
Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology
A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:
  • Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
  • Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
  • Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
  • Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
  • Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
  • Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
  • Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
  • Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.
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About NAVA Software Solutions

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NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

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