Senior Statistician / Data Scientist (Poker Analytics) (Remote)

The Role

Technical Challenges You'll Solve

• Variance Reduction in Stochastic Games: Implement counterfactual value estimation techniques (AIVAT, MAVAT) to reduce the inherent variance in poker outcomes while preserving unbiasedness in win-rate estimation
• Stratified Sampling Optimization: Design efficient sampling algorithms that minimize MSE while respecting computational constraints, potentially using importance sampling and variance-aware stratification
• All-in EV Adjustments: Develop robust methodologies for equity calculation and integration with partial observability in different game variants
• Statistical Confidence Metrics: Create mathematically sound approaches to confidence interval estimation that account for auto-correlation and heteroscedasticity in poker hand sequences

Key Responsibilities

• Algorithm Development: Implement variance reduction techniques with provable statistical properties and computational efficiency
• Monte Carlo Method Optimization: Enhance simulation approaches to achieve maximum statistical power with minimal computational resources
• Statistical Validation: Design rigorous testing frameworks to verify the statistical properties of implemented methods
• Codebase Improvement: Integrate statistical innovations into our Python-based analysis pipeline with proper vectorization and optimization
• Cross-team Collaboration: Work with engineers to ensure statistical methods scale appropriately with dataset size and computational resources

Requirements

• Academic & Domain Knowledge
  • Advanced degree in Statistics, Mathematics, or related quantitative field with strong focus on stochastic processes and estimation theory
  • Solid understanding of statistical inference, particularly in high-variance domains
  • Substantive poker knowledge including equity calculation, game theory optimal concepts, and hand range analysis
• Technical Expertise
  • Advanced Python programming with demonstrable experience in scientific computing (NumPy, SciPy, statsmodels)
  • Proficiency in efficient algorithm implementation for large-scale statistical computation
  • Experience with version control, testing frameworks, and reproducible research practices
• Problem-Solving Focus
  • Ability to develop novel mathematical approaches to unusual statistical estimation problems
  • Capacity to balance theoretical correctness with practical implementation concerns
  • Self-directed research capabilities with minimal supervision