R Modeling & Simulation Analyst - Part-time/fulltime - Worldwide

In search of a modeling-focused R contractor to help improve performance in daily fantasy sports contest play and sports betting. This role centers on building and refining Monte Carlo simulations, conducting backtests, and applying game theory principles to strengthen lineup selection and betting strategies.
You’ll turn complex data into actionable strategies by improving predictive models, optimizing simulation parameters and designing new algorithms and decision frameworks. The ideal candidate is a data-driven thinker with strong R experience and a passion for statistical modeling. General sports knowledge (especially NFL, NBA, MLB, UFC and GOLF) is a plus, but not a requirement.

Key Responsibilities:

• Design and improve monte-carlo based simulations
• Enhance player-level correlation and clustering techniques
• Build and tune parameter optimization routines
• Conduct backtests to validate performance strategies
• Collaborate on new modeling logic and simulation frameworks
• Assist with data wrangling, cleanup and structuring

Requirements:

• Strong background in data science, statistics, or applied modeling
• Advanced R experience (especially data.table, simulation libraries, modeling workflows)
• Familiarity with simulation design, uncertainty modeling, and strategy evaluation
• Highly organized, self-directed, and conscientious
• Comfortable with async collaboration and weekly check-ins

Nice to Have:

• Sports analytics and predictive model generation experience
• Experience with machine learning for predictive modeling or scenario evaluation
• Web scraping or API data collection experience
• Experience improving the speed and memory-efficiency of R codebases (profvis, vectorization, data.table tuning, etc)
• General sports knowledge (not a requirement)

Engagement:

Part-time (20 hours per week) or full-time (40 hours per week)