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Internship Data Labeling Jobs in Florida (NOW HIRING)

EdgeUno

IT Support Intern

Miami, FL · On-site

$14.25 - $19.25/hr

We provide connectivity, IP transit, private networks, data center, bare metal, and cloud solutions ... Help prepare, organize, label, and maintain cables, adapters, devices, and related technical ...

EdgeUno

IT Support Intern

Miami, FL · On-site

$14.25 - $19.25/hr

We provide connectivity, IP transit, private networks, data center, bare metal, and cloud solutions ... Help prepare, organize, label, and maintain cables, adapters, devices, and related technical ...

Edgeuno

IT Support Intern

Miami, FL · On-site

$14.25 - $19.25/hr

We provide connectivity, IP transit, private networks, data center, bare metal, and cloud solutions ... Help prepare, organize, label, and maintain cables, adapters, devices, and related technical ...

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All JobsData Labeling JobsInternship Data Labeling JobsInternship Data Labeling Jobs in Florida

Internship Data Labeling information

What is the difference between Internship Data Labeling vs Data Labeling Specialist?

AspectInternship Data LabelingData Labeling Specialist
CredentialsTypically students or entry-level with basic skillsRelevant experience or certifications in data annotation
Work EnvironmentInternship programs, often in tech or AI companiesFull-time or freelance roles in data annotation firms or tech companies
Industry UsageUsed as training or entry-level positionProfessional role with ongoing responsibilities
Search & Comparison IntentUnderstanding entry-level opportunities and trainingClarifying professional roles and career progression

Internship Data Labeling is an entry-level position designed for students or beginners gaining experience in data annotation. In contrast, Data Labeling Specialist is a professional role requiring prior experience or certifications, with more responsibility and independence. Internships serve as training grounds, while specialists handle ongoing data labeling tasks in a professional setting.

What types of tasks can I expect to handle daily as a Data Labeling Intern?

As a Data Labeling Intern, your daily tasks typically include reviewing and accurately tagging data such as images, audio, or text according to specific guidelines provided by the company or project. You may use specialized annotation tools and work closely with data scientists, engineers, or QA teams to ensure high-quality, consistent labeling. Attention to detail is crucial, as your work directly impacts the performance of machine learning models. You might also participate in periodic team meetings to discuss challenges, clarify instructions, and receive feedback to improve labeling accuracy.

What is an internship in data labeling?

An internship in data labeling involves assisting in the process of categorizing and annotating data, such as images, text, or audio, to help train machine learning models. Interns typically use specialized software to tag or classify data according to specific guidelines provided by the company or research team. This role is crucial for developing accurate artificial intelligence systems, as high-quality labeled data improves model performance. Data labeling internships are a great way to gain practical experience in the AI and machine learning field and learn more about data preprocessing workflows.

What are the key skills and qualifications needed to thrive as an Internship Data Labeling specialist, and why are they important?

To excel in an Internship Data Labeling role, you need strong attention to detail, basic analytical skills, and proficiency with data entry or spreadsheet tools, often supported by a high school diploma or current university enrollment. Familiarity with labeling platforms, annotation tools, and sometimes basic programming or database systems is beneficial. Reliability, communication, and the ability to follow instructions precisely are key soft skills for standing out in this position. These skills ensure data accuracy and consistency, which are crucial for developing reliable AI and machine learning models.
What are the most commonly searched types of Data Labeling jobs in Florida? The most popular types of Data Labeling jobs in Florida are:
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Internship Data Labeling jobs near you
Applied Data Scientist

Applied Data Scientist

Professional Staffing Services Group

Orlando, FL • On-site

$100K - $150K/yr

Full-time, Contractor

Posted 7 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.