Data Labelling Jobs in Kissimmee, FL (NOW HIRING)

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)

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.

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.

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.

• 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.

• 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.