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Remote Data Labeling Analyst Jobs in Milwaukee, WI

Analyze and annotate complex biological data sets, focusing on applications relevant to medicinal chemistry. * Provide feedback and domain-specific insights to improve AI models in computational ...

DemandFactor is a data-driven B2B growth and demand-generation company helping brands find, engage ... Manage marketing KPIs, analytics, attribution, and budget * Build and grow the marketing team What ...

Track carrier time in transit from shipment to delivery on an ongoing basis, managing large data ... Must be open to either working onsite at Jockey's corporate office (Kenosha, WI) or offsite remote ...

Enjoy the flexibility of remote work and the freedom to set your own schedule. This is an ... Proficient in financial analysis, financial modeling, data analysis, and other reasoning exercises ...

DevOps Engineer[remote]

Milwaukee, WI · Remote

$54 - $74/hr

Logic Apps, Azure Monitor/Log Analytics, Defender for Cloud, IoT suite, Advisor, etc ... Data Platform and Big Data: SQL Server, Azure SQL DB, HDInsight/Hadoop, Machine Learning, Cosmos DB

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Remote Data Labeling Analyst information

See Milwaukee, WI salary details

$33.5K

$81.4K

$134K

How much do remote data labeling analyst jobs pay per year?

As of Aug 11, 2026, the average yearly pay for remote data labeling analyst in Milwaukee, WI is $81,421.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,600.00 and $95,600.00 per year, depending on experience, location, and employer.

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 Milwaukee, WI? The most popular types of Data Labeling Analyst jobs in Milwaukee, WI are:
What job categories do people searching Remote Data Labeling Analyst jobs in Milwaukee, WI look for? The top searched job categories for Remote Data Labeling Analyst jobs in Milwaukee, WI are:
What cities near Milwaukee, WI are hiring for Remote Data Labeling Analyst jobs? Cities near Milwaukee, WI with the most Remote Data Labeling Analyst job openings:
Infographic showing various Remote Data Labeling Analyst job openings in Milwaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $81,421 per year, or $39.1 per hour.

Digital Chemistry Specialist - Remote

micro1 AI

Milwaukee, WI • Remote

$90 - $120/hr

Part-time

Posted 15 days ago


Job description

Role Title: Bioinformatics Scientist


Role Type: Contractor


Location: Remote


micro1 is engaging Bioinformatics Scientists to contribute their specialized expertise to a customer's innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze complex datasets related to medicinal chemistry using advanced bioinformatics methodologies.
  2. Provide detailed scientific input and content to support the development and training of AI models.
  3. Curate, annotate, and validate datasets relevant to drug discovery and molecular analysis.
  4. Evaluate and synthesize findings from biological, chemical, and clinical data sources.
  5. Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry.
  6. Assess AI-generated outputs for scientific accuracy, relevance, and reliability.
  7. Deliver comprehensive written feedback and actionable recommendations for model improvement.


Preferred Qualifications

  1. Advanced degree (e.g., PhD or MSc) in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related discipline.
  2. In-depth knowledge of medicinal chemistry concepts, including structure-activity relationships and drug design principles.
  3. Demonstrated experience in handling and interpreting large-scale omics or cheminformatics datasets.
  4. Familiarity with software tools, databases, and programming languages commonly used in bioinformatics (e.g., Python, R, RDKit, KNIME).
  5. Strong scientific communication skills, with the ability to clearly articulate complex ideas and technical concepts.
  6. Proven track record of contributing to research projects at the intersection of biology, chemistry, and data science.
  7. Experience collaborating in multidisciplinary or remote project environments is advantageous.