Python Simulation Engineer | Casino & Game Theory Modeling Expert (Edge Case Testing)

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Job Description We are looking for an experienced Python Simulation Engineer to build high-performance simulations for a casino-style crash game. This role involves designing, implementing, and optimizing probability-based simulations that model player behavior, betting strategies, house profitability, and edge case testing. Project Overview - Develop Python-based game simulations that model crash game mechanics - Implement probabilistic models using exponential decay and Monte Carlo simulations - Simulate player betting behavior using real-world wagering data - Optimize simulation mechanics for efficiency, scalability, and performance - Analyze house profitability vs. player experience across different rule sets - Perform extensive edge case testing to ensure simulation robustness in extreme scenarios (bankroll depletion, prolonged player win streaks, abnormal multiplier distributions) Required Skills & Expertise - Python Development (NumPy, pandas, multiprocessing) - Probabilistic & Statistical Modeling (Exponential decay) - Game Theory & Risk Modeling - Casinos/Game Simulations (experience in casino gaming, betting, or financial risk analysis preferred) - Monte Carlo Simulations (or other stochastic modeling methods) - Data Analysis & Visualization (Matplotlib, Seaborn) - Performance Optimization (efficient code execution for large simulations) - Edge Case Testing & Debugging Preferred Qualifications - Experience working with casino games, financial risk models, or sports betting analytics - Background in quantitative finance, gaming data science, or applied mathematics - Knowledge of player psychology and retention modeling - Experience with cloud-based simulation deployment (optional, preferred) - Familiarity with unit testing, scenario stress testing, and debugging simulation failures What We Provide - A well-defined framework for the crash game simulation - Access to existing datasets & parameters for testing - Competitive compensation for high-quality work Deliverables Fully functional Python-based simulation that supports: - Multiple game modes - Implements different house edge scenarios - Simulates player betting strategies according to real-world data - Generates realistic multiplier outcomes - Incorporates house reserve management mechanics, including cooldowns and capital infusion triggers Edge Case Testing & System Stability Reports: - Identify and stress test critical edge cases (prolonged win/loss streaks, extreme multiplier runs, bankroll depletion) - Ensure the simulation remains stable and balanced over long runs (100,000+ rounds) - Provide a report on findings and adjustments made to improve accuracy Comprehensive Data Exports & Insights: - Store round-by-round results in a structured format (bets, payouts, house revenue, rake collected, etc.) Basic Dashboard for Parameter Adjustments: - Simple interface to modify key settings (house edge, cooldown effects, multiplier probabilities) and re-run simulations Edge Case Testing Requirements - Test bankroll depletion scenarios (e.g. player funds running out due to strategy failures) - Stress-test prolonged player win/loss streaks and their impact on reserves - Analyze outlier multipliers (e.g. extended periods of low or high multipliers) - Ensure game fairness and expected profit margins under extreme conditions - Check system stability when running 1,000,000+ rounds at high speed How to Apply If you're an expert in Python, probability modeling, and simulations, we'd love to hear from you. To apply, please include: - A brief summary of your experience with game simulations, probabilistic modeling, or casino/betting mechanics - Links to any relevant projects or GitHub repositories - A short explanation of how you would approach edge case testing for this simulation Job Type & Budget: Freelance / Contract-Based Budget: Open to proposals - we will review based on experience, skills, and project scope. Project Duration: 2-4 Weeks

Keyword: Cloud

Python Data Analysis Statistics Game Theory Performance Optimization