Data Scientist

Summary    We are seeking talented individuals to join our Data Science team, developing machine learning solutions and analytics capabilities that power our Intelligence Platform for casino clients. The Data Scientist role builds and maintains predictive models, anomaly detection systems, and analytical tools that help casinos optimize player development, gaming operations, and compliance monitoring. This role will work closely with data engineers, product teams, and fellow data scientists to design and deploy data-driven solutions. The Data Scientist is responsible for developing models across the full lifecycle—from exploratory analysis and feature engineering through production deployment and ongoing monitoring. Essential Duties and Responsibilities

Build and maintain statistical models, machine learning algorithms, and predictive analytics using gaming and behavioral data (segmentation, churn prediction and lifetime value models)

Create anomaly detection systems for fraud and compliance monitoring

Collaborate with data engineers to collect and preprocess data, and build and maintain data pipelines

Design A/B testing frameworks and experiment analyses to test hypotheses and measure the effectiveness of solutions

Document models and use data visualization tools to communicate insights and findings to stakeholders

Requirements

BS/MS in a quantitative field or equivalent experience; years of experience commensurate with level

Strong Python programming skills and proficiency in SQL for data extraction and analysis

Strong foundation in statistics and experimentation: hypothesis testing, probability distributions, regression analysis, metric design, etc.

Experience with machine learning frameworks (scikit-learn, pandas) and deep learning libraries (PyTorch or TensorFlow), along with fundamental ML concepts (model evaluation, cross-validation, feature engineering)

Strong communication and story-telling skills, and ability to work collaboratively with cross-functional teams

Intellectual curiosity and comfort with ambiguity; we're building new things, not following playbooks

Preferred Experience

Experience with cloud platforms, especially AWS (S3, SageMaker, Lambda, etc)

Exposure to time-series analysis, survival models, or probabilistic modeling

Familiarity with model lifecycle management, CI/CD, containers, model monitoring, and feature stores

Exposure to causal inference concepts (A/B testing, uplift modeling, experimental vs. observational data)

Experience with data visualization (matplotlib, seaborn, plotly, etc.)