We're seeking an exceptional statistician to tackle complex variance reduction problems in poker analytics. This role focuses on implementing advanced statistical methodologies to achieve reliable win-rate estimation in the presence of high variance - one of the fundamental challenges in poker analysis. You'll work on our Acebench framework, developing mathematically rigorous approaches to EV estimation across multiple poker variants while handling computational constraints of large-scale evaluations.
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
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
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
This role offers the opportunity to solve mathematically interesting problems at the intersection of game theory, statistical inference, and computational optimization - with direct applications to measurable performance evaluation in poker.