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Contract Data Labeler Jobs in Florida (NOW HIRING)

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Contract Data Labeler information

What is a contract data labeler?

Contract data labelers are professionals hired on a temporary or project basis to annotate and categorize data, such as images, text, or audio, for use in machine learning and artificial intelligence projects. Their main responsibility is to accurately tag or label data according to specific guidelines so that algorithms can learn to recognize patterns. As contractors, they typically work remotely and are paid per task, hour, or project. This role is essential for companies developing AI models that require large sets of accurately labeled data.

What are the key skills and qualifications needed to thrive as a contract data labeler?

To thrive as a Contract Data Labeler, you need strong attention to detail, accuracy, and basic data management skills, often supported by a high school diploma or equivalent. Familiarity with data annotation tools, spreadsheet software, and sometimes project management platforms is typically required. Excellent focus, time management, and the ability to follow detailed instructions are crucial soft skills in this role. These skills ensure labeled data is reliable and consistent, which is vital for training high-quality AI and machine learning models.

What are some common challenges faced by contract data labelers, and how can they be addressed?

Contract Data Labelers often work with large volumes of diverse data, which can lead to repetitive tasks and fatigue. Ensuring accuracy and consistency in labeling is crucial, as errors can significantly impact machine learning outcomes. To address these challenges, it's important to take regular breaks, follow detailed guidelines, and communicate with team members or project leads when ambiguities arise. Many teams use quality assurance checks and feedback sessions to help labelers maintain high standards and continuously improve their work.

What is the difference between Contract Data Labeler vs Data Annotator?

AspectContract Data LabelerData Annotator
CredentialsHigh school diploma or equivalent; basic computer skillsHigh school diploma or equivalent; attention to detail
Work EnvironmentRemote or on-site; tech companies, AI firmsRemote or on-site; AI and machine learning projects
Industry UsageUsed in AI training, machine learning datasetsUsed in AI, computer vision, NLP projects
Job FocusLabeling data for machine learning modelsAnnotating data to improve AI algorithms

The Contract Data Labeler and Data Annotator roles are similar, both involve labeling data for AI training. The main difference lies in terminology and specific project focus, but they often share similar credentials, work environments, and industry applications.

What are the most commonly searched types of Data Labeler jobs in Florida?

The most popular types of Data Labeler jobs in Florida are:

What are popular job titles related to Contract Data Labeler jobs in Florida?

For Contract Data Labeler jobs in Florida, the most frequently searched job titles are:

What job categories do people searching Contract Data Labeler jobs in Florida look for?

The top searched job categories for Contract Data Labeler jobs in Florida are:

What cities in Florida are hiring for Contract Data Labeler jobs?

Cities in Florida with the most Contract Data Labeler job openings:

Contract Data Labeler jobs near you
Infographic showing various Contract Data Labeler job openings in Florida as of July 2026, with employment types broken down into 31% Full Time, 20% Part Time, and 49% Contract. Highlights an 100% In-person job distribution.

Applied Data Scientist

Professional Staffing Services Group Inc.

Orlando, FL • On-site

$100 - $150/hr

Other

Re-posted 5 days ago

Job description

Applied Data Scientist - Contract to Hire

Location: Florida (Remote but will need to travel to Orlando for your first day, and for occasional meetings and trainings.)

Employment Type: Full-Time, Pay: ~ 100K-150K

Sponsorship: Not Available (Now or in the future)

About The Company

Our client drives innovative, data‑driven insights and scalable AI solutions across the entertainment ecosystem. The Data Science team partners with data engineering, marketing, product, and executive teams to transform audience data into actionable strategies and operational products.

A successful Applied Data Scientist thrives on both analytical creativity and production rigor. As a key member of our client's team, you will own end‑to‑end modeling and deployment work-from the conceptual framing of business problems to data ingestion, model development, and reliable production delivery. Your work will directly shape how our company delivers value to clients and internal stakeholders.

Position Summary & Location Requirements

This is a Florida-based role. While the day-to-day work offers remote flexibility, candidates must reside in the state of Florida and meet the following travel requirements:

  • Day One: Ability to travel to Orlando, FL for your first day/onboarding.
  • Ongoing: Ability to travel to Orlando on occasion for collaborative meetings, trainings, and to support business needs.
Key Responsibilities

In this role, you will bridge the gap between business strategy and technical execution. Specifically, you will:

  • Model & Solution Development: Translate ambiguous business questions into structured analytical and ML solutions. Develop, validate, and optimize models impacting forecasting, segmentation, personalization, recommendation, or operational efficiency.
  • Production & MLOps: Build production‑ready pipelines and deploy models into scalable environments using robust MLOps practices (CI/CD, automated testing, monitoring), ensuring long‑term lifecycle maintenance.
  • Collaboration & Communication: Partner cross‑functionally to bridge business requirements and technical design. Communicate insights and technical decisions clearly to both technical and non‑technical stakeholders.
  • Documentation & Standards: Document all models, pipelines, and deployment processes comprehensively to ensure maintainability, reproducibility, and knowledge sharing.
  • Innovation: Stay ahead of emerging tools, techniques, and frameworks in ML/AI to influence best practices across the organization.
Core Qualifications
  • Education: Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Professional Experience: 5+ years of industry experience (excluding internships) in data science and machine learning, including proven ownership of model productization, monitoring, and iterative improvement.
  • Core ML Experience: 3+ years of building machine learning models for business applications (outside of academia), with deep expertise in both supervised and unsupervised learning algorithms.
  • Technical Stack:
  • Python: Strong programming skills with hands‑on experience building, training, deploying, and monitoring ML models.
  • SQL: 2+ years of experience with database querying, data preparation, and analysis.
  • Data Warehousing: Working knowledge of large‑scale platforms (e.g., Snowflake, SQL Server, BigQuery, Redshift).
  • Cloud Platforms: Familiarity with cloud environments (AWS, Azure, or GCP) and designing end‑to‑end ML pipelines from ingestion to production serving.
  • Execution Skills: Outstanding analytical skills to diagnose and resolve complex system issues, with a proven ability to manage multiple projects and prioritize tasks effectively.
What Sets You Apart (Preferred Qualifications)
  • Advanced Degree: Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Domain Expertise: Industry experience in entertainment or e-commerce, including domains such as theme parks, hospitality, live performances, ticketing, or retail marketplaces.
  • Advanced ML Architectures: Hands‑on experience designing and deploying recommendation models (collaborative filtering, content‑based, transformer‑based) or working with data labeling, taxonomy design, and classification frameworks.
  • Generative AI: Familiarity with GenAI techniques, language modeling, or frameworks like AWS Bedrock and Hugging Face.
  • Deep MLOps Tooling: Advanced experience with tools like SageMaker, Lambda, Airflow, or MLflow, and the ability to guide architectural/strategic decisions for ML infrastructure.
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