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Ai Data Labeling Jobs in Minnesota (NOW HIRING)

RELX

AI Data Analyst

Minneapolis, MN · On-site

Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning ... Help ensure data included in AI retrieval scenarios is appropriate, governed, and up to date.

Medtronic

Labeling Specialist

Moundsview, MN

Ability to leverage data analytics, automation, and AI tools to improve labeling efficiency, accuracy, and compliance. For Baccalaureate degrees earned outside of the United States, a degree that ...

Medtronic

Labeling Specialist

Moundsview, MN · On-site

Ability to leverage data analytics, automation, and AI tools to improve labeling efficiency, accuracy, and compliance. For Baccalaureate degrees earned outside of the United States, a degree that ...

Medtronic

Labeling Specialist

Moundsview, MN

Ability to leverage data analytics, automation, and AI tools to improve labeling efficiency, accuracy, and compliance. For Baccalaureate degrees earned outside of the United States, a degree that ...

Accenture

Data Protection Consultant

Minneapolis, MN

$112K - $133K/yr

Design and implement Microsoft Purview solutions (e.g., sensitivity labeling strategies, advanced ... Build awareness and controls for emerging AI and agentic AI security considerations (e.g., Security ...

Peak Systems

Be Seen First

Data Center Technician

Eagan, MN · On-site

$30 - $40/hr

Organize, route, and label data and power cables to maintain a clean, efficient, and safe ... Whether you're looking to expand your experience in data centers, AI infrastructure, field services ...

QBP

Sr Data Engineer BI

Bloomington, MN · Hybrid

$120K - $130K/hr

AI/ML Development & Agentic Workflows [15%] * Design, develop, train, and deploy machine learning ... Partner with InfoSec to implement role-based access, row-level security, and sensitivity labeling ...

Quality Bicycle Products

Sr Data Engineer BI

Bloomington, MN · On-site

$110K - $150K/yr

AI/ML Development & Agentic Workflows [15%] * Design, develop, train, and deploy machine learning ... Partner with InfoSec to implement role-based access, row-level security, and sensitivity labeling ...

U.S. Bank

AI Red Team Lead Engineer

Minneapolis, MN · On-site

$107K - $140K/yr

Data ingestion, labeling, and governance controls * Design and execute AI-specific threat emulation aligned to real-world adversaries, misuse scenarios, and emerging attack techniques (e.g., prompt ...

US Bank

AI Red Team Lead Engineer

Minneapolis, MN · On-site

$107K - $140K/yr

Data ingestion, labeling, and governance controls * Design and execute AI-specific threat emulation aligned to real-world adversaries, misuse scenarios, and emerging attack techniques (e.g., prompt ...

Deloitte

Cyber Data Protection Manager

Minneapolis, MN · Remote

$115K - $156K/yr

DLP, sensitivity labels, data classification, DSPM, DSPM for AI, on-demand classification, or related Microsoft 365 data security capabilities * Knowledge of AI security and governance concepts ...

QBP

BI Application Manager

Bloomington, MN · Hybrid

$140K - $150K/hr

... BI, data, and AI capabilities. This player-coach leader manages a high-impact team, while ... Partner with InfoSec on role-based access, row-level security, and sensitivity labeling for data ...

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All JobsAi Data Labeling JobsAi Data Labeling Jobs in Minnesota

Ai Data Labeling information

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior data scientist, or machine learning executive, often requiring advanced skills, extensive experience, and sometimes equity or bonuses. These roles usually involve leading projects, developing algorithms, and strategic decision-making in AI development, with compensation reflecting the seniority and impact of the position.

What is an AI Data Labeling job?

An AI Data Labeling job involves annotating or tagging data (such as images, text, audio, or video) to train machine learning models. Labelers categorize, classify, or highlight data based on specific guidelines to help AI understand patterns and make accurate predictions. This process is crucial for supervised learning, where models learn from labeled examples. AI Data Labeling jobs are common in industries like healthcare, finance, and autonomous vehicles. Attention to detail and consistency are key skills for success in this role.

What are the key skills and qualifications needed to thrive in the Ai Data Labeling position, and why are they important?

To thrive as an AI Data Labeling professional, you need strong attention to detail, analytical thinking, and the ability to follow precise guidelines, typically backed by a high school diploma or higher. Familiarity with annotation tools such as Labelbox, Supervisely, or internal labeling platforms, as well as basic understanding of data privacy practices, is often required. Patience, reliability, and good communication skills are important soft skills for consistently delivering high-quality labeled datasets and working effectively with team members. These skills ensure accurate data preparation for training AI models, directly impacting the model’s performance and the success of machine learning projects.

How much do AI labelers make?

AI data labelers typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the labeling tasks. Many positions are freelance or part-time, often requiring familiarity with labeling tools and attention to detail.

What does an AI data labeler do?

An AI data labeler is responsible for annotating and categorizing data such as images, videos, or text to help train machine learning models. This role requires attention to detail and familiarity with labeling tools, often working remotely with flexible schedules. Accurate labeling is essential for improving AI system performance.

Is data labelling a good career?

Data labeling is a viable entry-level job in the AI industry, involving annotating data to train machine learning models. It requires attention to detail and familiarity with labeling tools, and can offer flexible schedules and remote work options. However, it is often considered a stepping stone to more advanced roles in data science or AI development.

What are typical daily tasks for an AI Data Labeling professional?

As an AI Data Labeling professional, your primary responsibilities include reviewing raw images, audio, or text data and accurately tagging or classifying them based on set guidelines provided by your employer. You may also be required to flag ambiguous cases or data anomalies and provide feedback to improve labeling instructions. Collaboration with data scientists or machine learning engineers is common to ensure your work aligns with project needs. Maintaining high accuracy while meeting productivity goals is essential for success in this role.

What are the most commonly searched types of Ai Data Labeling jobs in Minnesota? The most popular types of Ai Data Labeling jobs in Minnesota are:
What are popular job titles related to Ai Data Labeling jobs in Minnesota? For Ai Data Labeling jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Ai Data Labeling jobs in Minnesota look for? The top searched job categories for Ai Data Labeling jobs in Minnesota are:
What cities in Minnesota are hiring for Ai Data Labeling jobs? Cities in Minnesota with the most Ai Data Labeling job openings:
Ai Data Labeling jobs near you
Infographic showing various Ai Data Labeling job openings in Minnesota as of July 2026, with employment types broken down into 66% Full Time, and 34% Contract. Highlights an 100% In-person job distribution.

AI Data Analyst

RELX

Minneapolis, MN • On-site

Full-time

Medical, Life

Re-posted 6 days ago

Job description

Are you passionate about improving data quality and readiness to unlock the full potential of AI solutions?

Do you enjoy collaborating across teams to ensure data is structured, governed, and usable for intelligent systems?

About the Business:

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below,

https://risk.lexisnexis.com

About the Team:

We are a newly formed Enterprise AI team focused on enabling agent-based solutions across the organization. We build and manage the environments, platforms, and guardrails that allow teams to create, test, and scale AI agents safely and efficiently turning experimentation into real business impact.

We're a team of curious builders and operators who are constantly exploring, learning, and applying new AI tools and approaches to solve real-world problems and improve how work gets done.

About the Role:

We are seeking an AI Data Analyst to support teams in preparing and maintaining AIready data for use in AI tools, copilots, and intelligent agents. This role focuses on data readiness, quality, metadata, and governance, helping teams understand how to structure, document, and manage their data so it can be safely and effectively used by AI systems.

The AI Data Analyst partners with data engineering, AI, and governance teams to assess data readiness, identify gaps and recommend improvements. This role does not own endtoend data pipelines and is not expected to be a deep technical expert in RAG or embeddings, but should have a solid working understanding of AIdriven data needs.

Responsibilities:

AI Data Readiness Support

  • Work with product and delivery teams to assess whether datasets and content are fit for AI use cases.
  • Help teams understand and apply AI data readiness standards, including quality, freshness, metadata, and access expectations.
  • Identify common data issues that impact AI outcomes (e.g., stale data, unclear ownership, missing metadata) and recommend remediation steps.
  • Contribute to repeatable checklists, guidance, or documentation that help teams prepare data for AI.

Data Quality & Relevance

  • Support data quality checks focused on accuracy, completeness, consistency, and timeliness for AIconsumed data.
  • Assist in monitoring and validating data freshness and relevance, escalating issues to engineering or data owners as needed.
  • Help teams improve data clarity and usability to reduce ambiguity in AI outputs.

Metadata & Semantic Enablement

  • Assist teams in improving metadata, documentation, and business descriptions so AI systems can better interpret content.
  • Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning (in coordination with engineering teams).
  • Promote good content hygiene practices (clear structure, consistent naming, wellscoped documents).

AI Data Sources & Retrieval (Support Role)

  • Support the upkeep and documentation of approved data sources used by AI solutions.
  • Help ensure data included in AI retrieval scenarios is appropriate, governed, and up to date.
  • Collaborate with AI and platform teams on data inclusion/exclusion decisions without owning technical implementation.

Governance, Lineage & Compliance Awareness

  • Help teams align AIconsumed data with enterprise governance requirements, including classification, access controls, and retention.
  • Support basic data lineage and ownership documentation for AIrelevant datasets.
  • Partner with governance and security teams by surfacing risks or gaps; does not act as final approval authority.

What This Role Does Not Own

  • Does not design or own endtoend production data pipelines.
  • Does not act as the primary technical owner for RAG frameworks, vector databases, or embedding strategies.
  • Does not make final governance or compliance decisions independently.

Requirements:

  • Proven experience in data analysis, analytics engineering, data operations, or data quality roles.
  • Good understanding of data quality principles and how poor data impacts downstream systems.
  • Experience working with structured and unstructured data (tables, files, documents, knowledge assets).
  • Proficiency in SQL and comfort investigating data issues.
  • Familiarity with data governance fundamentals (classification, access controls, ownership, retention).
  • Strong communication skills and ability to explain data concepts to nontechnical stakeholders.

Preferred Qualifications

  • Exposure to AIenabled products, copilots, or searchbased solutions.
  • Basic familiarity with AI data concepts such as semantic search, embeddings, or retrieval patterns.
  • Experience working in enterprise or regulated environments.
  • Experience contributing to standards, playbooks, or shared data practices.

What Success Looks Like

  • Teams can reliably prepare datasets that meet AI readiness expectations with less rework.
  • AI solutions benefit from more relevant, uptodate, and understandable data.
  • Clear ownership and documentation exist for data used by AI systems.
  • Strong collaboration between delivery teams, data engineering, and governance.

Working for You:

We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

  • Medical Inpatient and Outpatient Insurance: Coverage for your healthcare needs.
  • Life Assurance Policies: Providing financial security for your loved ones.
  • Modern Family Benefits: Support for maternity, paternity, and adoption needs.
  • Long Service Award: Recognition for your dedication and loyalty.
  • Celebratory Allowance/Gifts: Marking special occasions to celebrate with you.
  • Flexible Benefits Plan : Offering you wider choice of services and products
  • Employee Assistance Program : Access support for personal and work-related challenges.
  • Flexible Working Arrangements: Balance work and personal life effectively.
  • Access to Learning and Development Resources: Empowering your professional growth.

Risk benefit statement
Learn more about the LexisNexis Risk team and how we work: https://relx.wd3.myworkdayjobs.com/RiskSolutions/page/21c296c982531000b79663f3194b0000

U.S. National Base Pay Range: $78,800 - $131,300. Geographic differentials may apply in some locations to better reflect local market rates. If performed in Ohio, the base pay range is $74,900 - $124,700. This job is eligible for an annual incentive bonus.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Formor please contact 1-855-833-5120.

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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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