Litehouse
Litehouse is an Experience Management Platform that will create, manage, and scale tomorrow’s smart city, stadium, and destination experiences. It began as interactive and immersive physical experiences with rich media marketing campaigns and is now becoming an integrated platform for deeply engaging customer experiences across connected spaces; physical, digital, and virtual. Litehouse will provide the end-to-end orchestration of data, systems, and applications to manage connected spaces as an integrated technology platform for The Anaheim Ducks, Honda Center Arena, and upcoming OCVIBE district.
Role
We're seeking an experienced Data Scientist to join our Data Platform team and lead the development of advanced analytics solutions for identity resolution and predictive modeling in the sports and entertainment venue space. You will build sophisticated probabilistic and deterministic matching algorithms to create unified fan profiles across millions of touchpoints while developing predictive models that enhance venue operations and fan experiences.
In this role, you will design and implement machine learning models that power real-time decision-making for crowd management, personalized fan experiences, and revenue optimization. Working with massive datasets from ticketing systems, mobile apps, in-venue sensors, and third-party platforms, you'll deliver actionable insights that transform how venues understand and engage with their audiences.
Responsibilities
- Design and implement identity resolution systems combining deterministic and probabilistic matching algorithms to unify fan profiles across multiple touchpoints
- Develop predictive models for fan behavior including attendance prediction, purchasing patterns, and personalization using advanced ML techniques
- Build real-time scoring engines for dynamic pricing, targeted marketing, and operational optimization
- Create anomaly detection systems for crowd management and security using streaming venue sensor data
- Implement feature engineering pipelines processing billions of events from ticketing, POS, mobile apps, and IoT devices
- Design experimentation frameworks including A/B testing and causal inference to measure impact of venue initiatives
- Work with computer vision models for crowd density estimation and queue length prediction
- Collaborate with engineering teams to productionize ML models ensuring sub-second inference latency
- Lead statistical analysis and create automated reporting systems for venue performance metrics
- Mentor team members and establish best practices for model validation and ethical AI
Requirements
- Master's degree in Data Science, Statistics, Computer Science, or related work experience
- 4+ years of hands-on experience building and deploying ML models in production environments
- Proven expertise in identity resolution, including deterministic matching and probabilistic record linkage techniques
- Advanced Python proficiency with NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow, and PySpark
- Strong statistical foundation in probabilistic modeling, Bayesian inference, time series analysis, and causal inference
- Deep ML expertise in common techniques for supervised/unsupervised learning, gradient boosting, neural networks, and ensemble methods
- Experience with big data processing and distributed computing for billion-scale datasets
- Real-time analytics experience building low-latency prediction systems for streaming applications
- MLOps knowledge including model deployment, monitoring, and lifecycle management (MLflow, Kubeflow)
- Expert SQL skills for complex analytical queries and data exploration
- Cloud platform experience with Azure ML, Databricks, or similar ML platforms
- Strong experimentation background in A/B testing, multi-armed bandits, and experimental design
- Data visualization skills for creating compelling narratives with Matplotlib, Plotly, or similar tools
- Excellent communication abilities to present technical findings to both technical and executive audiences
- Collaborative mindset with proven ability to work effectively in cross-functional teams
Nice to Have
- Experience in sports analytics, venue management, or entertainment industry
- Knowledge of ticketing systems (especially Ticketmaster) and venue operations
- Published research in identity resolution, entity matching, or predictive analytics
- Experience with reinforcement learning for dynamic pricing or resource optimization
- Familiarity with IoT data processing and edge computing
- Background in operations research or revenue management
- Contributions to open-source ML/data science projects
- Experience with geospatial analytics and location intelligence
Benefits
Personal
- Early stage, growth minded product environment
- Elevate your role and responsibilities as the team scales up and builds out
- Working on a one of a kind product
Employment
- Remote-first team
- Flexible time off
- Health benefits
- 401k matching
- Annual wellness stipend