Principal Research Scientist - Risk at Neara · LumiJobs
senior
$180k–$220k
H1B: no
Neara Principal Research Scientist - Risk at Neara responsible for contributing to applied research on system resilience, reliability, and policy. Develop predictive and decision models to support utilities managing capital expenditure.
Responsibilities
Contribute to Neara’s applied research function for infrastructure risk and resilience.
Establish rigorous standards for risk modeling, uncertainty quantification, and decision-making.
Develop predictive models for equipment damage, vegetation failure, outage likelihood and duration, and wildfire-related risk.
Design decision models that quantify the operational and economic consequences of resilience and reliability investments, including asset replacement, vegetation management, and network redesign.
Quantify the value of data including pole loading analysis, visual inspections, and the installation of new devices and remote sensing capabilities, both empirically and by analytical methods.
A central role in shaping Neara’s long-term product roadmap, analytical standards and customer advisory services.
Flexible hours and working arrangements designed to help you do your best work from where you work best.
Qualification
A PhD in statistics
Required
A PhD in statistics, applied mathematics, physics, computer science, operations research, engineering, or a comparable record of original research and applied technical work.
Proven track record of independently defining, driving, and executing a substantial research agenda as a senior individual contributor or industry principal, demonstrated through influential publications, patents, or production systems.
Recognized subject matter expert with deep technical authority and a history of setting research direction without needing direct management oversight.
Extensive experience in probabilistic modeling, uncertainty quantification, model validation, scientific computing capabilities and decision-making under incomplete information.
Substantial experience modeling operational systems. Particularly relevant topics include insurance or catastrophe modeling; weather prediction and climate hazard; outages in power systems; and reliability engineering.
