Applied Data Scientist

Responsibilities

• Model and 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 and 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 and communication: partner across functions to align business requirements with technical design; communicate insights and technical decisions clearly to technical and non‑technical stakeholders.
• Documentation and standards: document all models, pipelines, and deployment processes to ensure maintainability, reproducibility, and knowledge sharing.
• Innovation: stay current with emerging tools, techniques, and frameworks in ML/AI to influence best practices across the organization.

Requirements

• Education: Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Professional experience: 5+ years of industry experience in data science and machine learning, including ownership of model productization, monitoring, and iterative improvement.
• Core ML experience: 3+ years building ML models for business applications, with deep expertise in both supervised and unsupervised learning algorithms.
• 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 such as Snowflake, SQL Server, BigQuery, or Redshift.
• Cloud platforms: familiarity with AWS, Azure, or GCP and designing end-to-end ML pipelines from ingestion to production serving.
• Execution skills: outstanding analytical abilities to diagnose and resolve complex system issues, with a proven track record of managing multiple projects and prioritizing tasks.

Technologies

• Python
• SQL
• Snowflake
• SQL Server
• BigQuery
• Redshift
• AWS
• Azure
• GCP
• SageMaker
• Lambda
• Airflow
• MLflow
• AWS Bedrock
• Hugging Face

What Sets You Apart (Preferred Qualifications)

• Advanced degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Domain experience in entertainment or e-commerce, including theme parks, hospitality, live performances, ticketing, or retail marketplaces.
• Advanced ML architectures for recommendations (collaborative filtering, content-based, transformer-based) plus data labeling, taxonomy design, and classification frameworks.
• Generative AI familiarity with language modeling and frameworks like AWS Bedrock and Hugging Face.
• Deep MLOps tooling experience with SageMaker, Lambda, Airflow, or MLflow, with ability to guide ML infrastructure decisions.

Education

• Bachelor's degree preferred

Experience (Preferred)

• Data science: 5 years (Preferred)
• Machine learning: 5 years (Preferred)
• SQL: 2 years (Preferred)
• Python: 2 years (Preferred)
• Data warehouse: 2 years (Preferred)
• Cloud platforms: 2 years (Preferred)

Position Summary & Location Requirements

This is a Florida-based role with remote work flexibility. Day one onboarding requires travel to Orlando, FL, and occasional travel to Orlando for collaborative meetings, trainings, and business needs.

Work Location

In person

Compensation

Salary: USD 100,000 - 150,000 annually

About the Company

Professional Staffing Services' client delivers innovative, data driven insights and scalable AI solutions across the entertainment ecosystem. The Data Science team collaborates with data engineering, marketing, product, and executive teams to transform audience data into actionable strategies and operational products. The role emphasizes end-to-end modeling and production delivery from problem framing to data ingestion and model deployment, shaping value delivered to clients and internal stakeholders.