Data Science Analyst

Key Requirements2+ years of experience in data science, advanced analytics, or a similar analytical role \n

Strong Python (pandas, NumPy, PySpark) and advanced SQL skills \n

Hands-on Databricks experience, including notebooks, jobs, pipelines, and Unity Catalog \n

Hands-on experience building and validating predictive models such as regression, classification, time-series forecasting, or clustering \n

Working knowledge of experimental design, including A/B testing, holdouts, and causal measurement of lift \n

Daily, current use of AI-assisted development tools (Claude Code, GitHub Copilot), with the judgment to validate AI output \n

Experience building dashboards or reports in Power BI or Tableau \n

Excellent written and verbal communication skills, with the ability to explain complex analysis clearly to business and executive audiences \n

Strong visual storytelling: able to present workflows, model logic, and findings in diagrams non-technical stakeholders can follow \n

Must be authorized to work in the U.S. without sponsorship \n

Education Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field, or equivalent experience \n

Desired, But Not Required Knowledge Casino gaming industry experience as an analyst, mathematician, or game designer (highly preferred), including understanding of return-to-player (RTP), theoretical win, and how rewards are distributed across player sessions \n

Familiarity with player tracking, slot telemetry, comp and offer optimization, or reinvestment modeling \n

Experience in hospitality, cruise, travel, retail, or loyalty analytics \n

Master’s degree in a quantitative field \n

Working knowledge of Alteryx \n

Experience deploying models into production, including monitoring, retraining, and MLflow \n

Experience with streaming or near real-time data (e.g., Kafka) and cloud platforms (Azure or AWS) \n

Familiarity with CRM, loyalty, or offer-management platforms, plus GitHub, Jira, and Confluence \n

Key Responsibilities Build and maintain machine learning and statistical models that support casino marketing and operations decisions \n

Run ad hoc exploratory data analysis (EDA) on large casino, loyalty, and guest datasets to find revenue and efficiency opportunities \n

Develop guest segmentation and demographic analysis to support personalized marketing and pricing models \n

Design and analyze experiments and holdout tests to measure the business impact of analytical work \n

Write Python and SQL in Databricks notebooks, scripts, and pipelines \n

Convert existing Alteryx workflows into maintainable Python notebooks, scripts, and Databricks pipelines \n

Build dashboards and recurring reports that give Casino Marketing, Operations, and Finance clear visibility into performance \n

Monitor data quality and work with Data Engineering to resolve gaps, latency, or schema issues \n

Use AI-assisted development tools (Claude Code, GitHub Copilot) to speed up model development, testing, documentation, and code review \n

Translate model outputs into plain recommendations for non-technical stakeholders and present findings to leadership \n

Document data definitions, model assumptions, and methodology so work is repeatable across teams and brands \n

Follow company data-handling, privacy, and security standards when working with guest and loyalty data