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Remote Ai Data Rater Jobs in Arizona (NOW HIRING)

Deloitte

GCP Manager

Tempe, AZ · On-site +1

... or remote client service delivery. Recruiting for this role ends on 06/30/2026. Work you'll do As a GCP Manager on the AI & Data team, you will be responsible for... * Drive solution reviews and ...

Data Annotation

$50 - $60/hr

Join our team to help train the next generation of AI while enjoying the flexibility of remote work ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

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Remote Ai Data Rater information

What is the difference between Remote Ai Data Rater vs Remote Content Moderator?

AspectRemote Ai Data RaterRemote Content Moderator
Required CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentOnline, flexible hours, task-basedOnline, flexible hours, task-based
Employer & IndustryTech companies, AI developmentSocial media, online platforms
Search & Comparison IntentData labeling, AI trainingContent review, policy enforcement

Remote Ai Data Raters focus on labeling and annotating data to train AI models, while Remote Content Moderators review user-generated content to enforce platform policies. Both roles require attention to detail and are performed online with flexible hours, but they serve different purposes within the tech industry.

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

To thrive as a Remote AI Data Rater, you need strong analytical skills, attention to detail, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with web-based evaluation platforms and tools, as well as adherence to specific project guidelines, is typically required. Excellent communication, time management, and the ability to work independently are valuable soft skills for this role. These abilities ensure accurate data labeling and evaluation, which are crucial for improving AI system performance.

What are some common challenges faced by Remote AI Data Raters, and how can they be overcome?

Remote AI Data Raters often encounter challenges such as maintaining consistent focus during repetitive tasks and accurately interpreting guidelines that may change depending on project requirements. Effective strategies to overcome these challenges include setting up a distraction-free workspace, taking regular short breaks to reduce fatigue, and actively participating in training sessions or forums provided by employers. Additionally, clear communication with team leads or project managers can help clarify ambiguous instructions and ensure alignment with quality standards.

What are Remote AI Data Raters?

Remote AI Data Raters are professionals who evaluate and annotate data such as text, images, or audio to help improve artificial intelligence systems. They work from home or any remote location, following specific guidelines to assess the relevance, accuracy, and quality of data used to train AI algorithms. Their feedback is crucial for machine learning models to better understand and process human language, visual content, or other data types. This role often requires attention to detail, strong communication skills, and the ability to follow detailed instructions.
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Remote Ai Data Rater jobs near you

Principal AI Data Scientist

MSR Technology Group

Phoenix, AZ • Remote

Full-time

Posted 12 days ago

Job description


Infomatics is partnered with a large retailer that is hiring a Principal AI Data Scientist on a direct hire/FTE basis near Phoenix, AZ. Can work remote. All applicants must be eligible & willing to be hired on W2.

You will lead various AI efforts involving computer vision, deep learning, and nlp in addition to other machine learning model builds. You will not only work on large scale projects to provide value to the customers but are also routinely involved in building our internal R&D capability to have an edge in the analytics industry. You will lead some of the most strategic and very complex problems.
Duties/Responsibilities:
  • Builds and validates machine learning models of high risk/reward problems utilizing large scale data from multiple data sources and methodologies.
  • Uses machine learning techniques to create data-driven solutions for various business use-cases.
  • Writes programs utilizing existing libraries and methodologies.
  • Interprets, communicates, and presents analytic results to C-Level executives and below.
  • Consistently collaborates with fellow data scientists, data engineers, business partners, project managers, cross-functional teams, key stakeholders, and other domains to drive business value.
  • Leads AI best practice sharing opportunities and knowledge of industry trends and innovations in data science.
  • Leads projects with external partners and vendors to develop solutions to meet business needs while resolving any issues that may arise.
  • Contributes to the organization's data strategy and roadmap.
  • Embeds and drives the organization with the most up-to-date AI methodology.
Qualifications:
  • Master's or PhD degree in a quantitative field with 5+ years of data science experience.
  • Applied expertise in artificial intelligence with experience applying natural language processing, computer vision (image processing), and deep leaning. Need to have the capability to leverage current mature mainstream AI application tools and methodology
  • Proficiency in machine learning with familiarity and actual applications of scikit-learn library machine learning techniques such as decision tree, gradient boosting, XGBoost, etc. for regression, classification, or segmentation problems.
  • Programming expertise in Python with familiarity with cloud environments (AWS, Databricks, etc.)
  • Ability to work with large data sets from multiple data sources
  • Ability to communicate complex analytics concepts and techniques to C-Level executives and below
  • Ability to work collaboratively with other data scientists, data engineers, multiple stakeholders across the business, and with external partners
  • Intellectual curiosity, a passion for data, and a results orientation.