Data Scientist I

What You’ll Deliver

• Data Science & Modeling
• Develop, validate, and deploy statistical and machine learning models
• Perform exploratory data analysis to identify trends, anomalies, and opportunities
• Build predictive models (e.g., revenue forecasting, member retention, segmentation)
• Design and evaluate experiments (A/B testing where applicable)
• Develop feature engineering strategies across large datasets
• Business & Analytical Translation
• Translate business requirements into analytical models and measurable outputs
• Define key metrics, KPIs, and analytical frameworks with stakeholders
• Provide data-driven recommendations to support strategic initiatives
• Support financial, marketing, and operations teams with advanced analytics
• AI & Advanced Analytics
• Support AI initiatives including recommendation models, automation, and agents
• Apply advanced techniques such as: Regression, classification, clustering, Time series forecasting, Natural language processing (where applicable)
• Evaluate model performance and continuously optimize
• Data Integration & Collaboration
• Work closely with Data Engineers to ensure data readiness and pipeline integrity
• Collaborate with BI Engineers to productionize models into reporting layers
• Ensure models integrate into enterprise data architecture and workflows
• Partner with QA to validate data accuracy and model outputs
• Data Governance & Quality
• Ensure data quality, consistency, and auditability of analytical outputs
• Document methodologies, assumptions, and model logic clearly
• Support compliance and audit requirements (SOC 2 alignment where applicable)

What You’ll Bring To The Team

• Programming: Python or R (required), SQL (advanced)
• Machine Learning / Statistical Modeling: scikit-learn, TensorFlow, PyTorch, or equivalent
• Data Platforms: Experience working with data warehouses (Snowflake, Azure, etc.)
• Visualization: Power BI, Tableau, or similar (for model output interpretation)
• Experience with: Cloud platforms (Azure, AWS), Hospitality, loyalty, or revenue analytics (strong plus)
• Data marts / Kimball methodology environments
• Familiarity with: Feature stores, MLOps, model deployment pipelines, AI-driven automation use cases