Senior Bayesian Risk Modeler

Job not on LinkedIn

November 8

☕ Washington – Remote

Although this job is listed in Washington, this company may be open to hiring remote in other states.

⏰ Full Time

🟠 Senior

🎲 Risk

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Technosylva

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Artificial Intelligence • SaaS • B2B

<Technosylva> is a provider of AI-driven wildfire and extreme weather risk mitigation software that delivers real-time forecasting, predictive simulations, and incident management tools for electric utilities, fire agencies, and insurers. Their cloud-based products (Wildfire Analyst, Tactical Analyst, fiResponse) offer situational awareness, operational decision support, and risk quantification to help customers plan, operate, and respond to wildfire and severe weather events.

51 - 200 employees

Founded 1997

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

📋 Description

• Redesign annualized risk frameworks using Monte Carlo simulation and Bayesian models. • Develop and validate probabilistic models that propagate uncertainty from various factors. • Build calibrated prediction systems where stated confidence intervals accurately reflect true uncertainty. • Optimize weather day selection algorithms using information-theoretic approaches. • Apply extreme value theory to better characterize tail risks from rare weather events.

🎯 Requirements

• Education: Ph.D. in Statistics, Biostatistics, Applied Mathematics, Computational Statistics, or related quantitative fields strongly preferred. • A master's degree with exceptional depth in probabilistic modeling and 10+ years of applied experience will be considered. • Professional Experience: 6+ years of experience in quantitative roles requiring sophisticated statistical modeling. • Proven track record of deploying probabilistic models in production environments. • Experience translating research-grade statistical methods into robust, scalable, and maintainable production systems. • Proven mastery of Bayesian inference methodology. • Hands-on experience implementing MCMC samplers and understanding their convergence properties. • Extensive experience designing and implementing Monte Carlo frameworks for uncertainty propagation. • Working knowledge of copula methods for modeling dependence structures. • Advanced Python skills with demonstrated ability to write clean, efficient code for complex statistical applications. • Comfortable working with large-scale geospatial and time-series datasets.

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development