Executive Director - Data Science

Job Description

Reporting to the VP of Casino Loyalty Marketing, this individual will own the data-science function for Wynn Las Vegas (“Wynn”) and be chiefly tasked with enabling the execution of Wynn’s gaming marketing campaigns (likely extending to include non-gaming in the future as the function progresses). Key functional responsibilities include:

• Develop 360-Degree Customer View: Collaborate with the development team to establish business and technical requirements for Wynn’s comprehensive customer view capability.
• Manage Analytical Data-Warehouse: Oversee Wynn’s proprietary analytical data warehouse, including data aggregation and prediction pipelines.
• Customer Value Metrics: In consultation with gaming and non-gaming business owners, develop customer value metrics (e.g., LTV) and associated segments; manage the modeling for LTV prediction and marketing offer derivation for various segments.
• Team Development: Hire and mentor junior data science experts focused on both gaming and non-gaming aspects of Wynn’s business.
• Machine Learning Predictions: Work with Wynn’s broader casino marketing departments to develop and productionize machine learning predictions to maximize customer LTV.
• Response Rate and Lift Modeling: Apply knowledge of response rate, incremental lift, preference modeling, and return on marketing reinvestment.
• Offer Personalization: Enhance offer attractiveness and optimization through both qualitative and quantitative personalization techniques.
• Campaign Design and Retargeting: Provide informed opinions on direct marketing campaign design and retargeting activities.
• Communication and Presentation: Exhibit a mature and confident presentation style, effectively communicating complex data science results across the organization.
• Business Trend Analysis: Review recent business trends to extract insights for data-driven predictions and marketing campaign adjustments; possess deep knowledge of A/B testing and propensity matching.
• Emerging Analytical Frameworks: Stay informed about emerging machine learning approaches and analytical frameworks/platforms, including AI deep learning, LLM, and multimodal models, and evaluate their potential applications and monetization strategies for Wynn’s products.

Qualifications

Technical Qualifications:

• Educational Background: Master’s or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field.

• Programming: Expert in Python with 8+ years of industry-level programming experience, including object-oriented programming. Proficiency in software engineering principles, algorithms, and data structures. Capable of conducting high-standard, detailed, hands-on code reviews for the data science team. Knowledge of R is a plus but not required.

• Machine Learning Models: Strong expertise in a wide array of traditional supervised machine learning models (e.g., logistic regression, XGBoost) and unsupervised algorithms (e.g., dimensionality reduction, clustering).

• Deep Learning Architectures: Experience with deep learning architectures (e.g., transformers) and frameworks (e.g., PyTorch, TensorFlow), and related experiment tracking libraries (e.g., WandB, MLflow).

• Distributed Computing and Cloud Platforms: Knowledge of distributed computing, cloud platforms (e.g., BigQuery, Snowflake, Databricks), and big data technologies like Spark.

• A/B Testing Design: Extensive experience in designing rigorous A/B tests and integrating them with model iterations to achieve measurable success.

• ML System Design: Proven experience in ML system design, orchestrating the productionalization of data science/ML products with high robustness and scalability.

• Data Visualization: Experience with data visualization tools (e.g., Tableau, PowerBI).

• CRM Applications: Experience with CRM applications like Salesforce and customer engagement platforms like Braze is a bonus.

Pivotal Experience & Expertise:

Data Science Expertise

• Demonstrated ability to define, implement, and improve data strategy, analytics, and transformation frameworks; sophisticated understanding of various pros/cons and ideal use cases for available tools and methodologies
• 10+ years of machine learning, advanced analytics, or data science experience
• Understanding of predictive analytics and modeling; experience impr